From 4c4f7599c93717c7e71302a433be993283932a2a Mon Sep 17 00:00:00 2001
From: bruAristimunha <a.bruno@aluno.ufabc.edu.br>
Date: Thu, 18 Apr 2024 17:16:33 +0200
Subject: [PATCH] replace the docs!

---
 docs/.buildinfo                               |     4 +
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 docs/README.html                              |   747 ++
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 docs/_static/jquery-3.6.0.js                  | 10881 ++++++++++++++++
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 docs/_static/styles/pydata-sphinx-theme.css   |     1 +
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 docs/_static/underscore-1.13.1.js             |  2042 +++
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diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html
new file mode 100644
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--- /dev/null
+++ b/docs/CONTRIBUTING.html
@@ -0,0 +1,653 @@
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+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="_static/moabb_logo.png" type="image/png">
+    <title>Contributing &#8212; moabb 1.0.0-dev documentation</title>
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+    <script src="https://cdn.datatables.net/v/dt/dt-2.0.4/b-3.0.2/b-html5-3.0.2/datatables.min.js"></script>
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+    <link rel="search" title="Search" href="search.html" />
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+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
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+<a class="navbar-brand logo" href="index.html">
+  
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+  
+  
+    
+    
+    
+    <img src="_static/moabb_logo.svg" class="logo__image only-light" alt="Logo image"/>
+    <script>document.write(`<img src="_static/moabb_logo.svg" class="logo__image only-dark" alt="Logo image"/>`);</script>
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+  
+  
+  <div class="col-lg-9 navbar-header-items">
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+    <div class="me-auto navbar-header-items__center">
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+                
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+                        Datasets
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+                        Overview
+                      </a>
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+document.write(`
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+document.write(`
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+        
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
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+     aria-level="1"
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+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
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+                      <a class="nav-link nav-internal" href="install/install.html">
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+                        Overview
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+                    
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+                      </a>
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+                
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+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
+                      </a>
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+                      <a class="nav-link nav-internal" href="api.html">
+                        API reference
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+                        Release notes
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+<script>
+document.write(`
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+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
+
+
+<nav aria-label="Breadcrumbs">
+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    <li class="breadcrumb-item active" aria-current="page">Contributing</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section id="contributing">
+<h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this heading">#</a></h1>
+<p>Contributions are always welcome, no matter how small!</p>
+<p>If you think you can help in any of the areas of MOABB (and we bet you can) or in any of
+the many areas that we haven’t yet thought of (and here we’re <em>sure</em> you can) then please
+check out our <a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/master/ROADMAP.md">roadmap</a>.</p>
+<p>Please note that it’s very important to us that we maintain a positive and supportive
+environment for everyone who wants to participate. When you join us we ask that you follow
+our <a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/master/CODE_OF_CONDUCT.md">code of conduct</a>
+in all interactions both on and offline.</p>
+<p>The following is a small set of guidelines for how to contribute to the project</p>
+<section id="where-to-start">
+<h2>Where to start<a class="headerlink" href="#where-to-start" title="Permalink to this heading">#</a></h2>
+<section id="id1">
+<h3>Code of Conduct<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h3>
+<p>This project adheres to the Contributor Covenant <a class="reference external" href="CODE_OF_CONDUCT.md">Code of Conduct</a>. By
+participating you are expected to adhere to these expectations. Please report unacceptable
+behavior to <a class="reference external" href="mailto:hi&#37;&#52;&#48;pushtheworld&#46;us">hi<span>&#64;</span>pushtheworld<span>&#46;</span>us</a></p>
+</section>
+<section id="contributing-on-github">
+<h3>Contributing on Github<a class="headerlink" href="#contributing-on-github" title="Permalink to this heading">#</a></h3>
+<p>If you’re new to Git and want to learn how to fork this repo, make your own additions, and
+include those additions in the master version of this project, check out this
+<a class="reference external" href="http://blog.davidecoppola.com/2016/11/howto-contribute-to-open-source-project-on-github/">great tutorial</a>.</p>
+</section>
+<section id="community">
+<h3>Community<a class="headerlink" href="#community" title="Permalink to this heading">#</a></h3>
+<p>This project is maintained by the <a class="reference external" href="www.neurotechx.com">NeuroTechX</a> community. Join the
+<a class="reference external" href="https://gitter.im/moabb_dev/community">Gitter</a>, where discussions about MOABB takes
+place.</p>
+</section>
+</section>
+<section id="how-can-i-contribute">
+<h2>How can I contribute?<a class="headerlink" href="#how-can-i-contribute" title="Permalink to this heading">#</a></h2>
+<p>If there’s a feature you’d be interested in building or you find a bug or have a
+suggestion on how to improve the project, go ahead! Let us know on the
+<a class="reference external" href="https://gitter.im/moabb_dev/community">Gitter</a> or <a class="reference external" href="../../issues">open an issue</a> so others
+can follow along and we’ll support you as much as we can. When you’re finished submit a
+pull request to the master branch referencing the specific issue you addressed.</p>
+<section id="steps-to-contribute">
+<h3>Steps to Contribute<a class="headerlink" href="#steps-to-contribute" title="Permalink to this heading">#</a></h3>
+<ol class="arabic simple">
+<li><p>Look for open issues or open one</p></li>
+<li><p>Discuss the problem and or propose a solution</p></li>
+<li><p>Fork it! (and clone fork locally)</p></li>
+<li><p>Branch from <code class="docutils literal notranslate"><span class="pre">develop</span></code>: <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">checkout</span> <span class="pre">--track</span> <span class="pre">develop</span></code></p></li>
+<li><p><a class="reference external" href="#setup-development-environment">Setup development environment</a></p></li>
+<li><p>Create your feature branch: <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">checkout</span> <span class="pre">-b</span> <span class="pre">my-new-feature</span></code></p></li>
+<li><p>Make changes</p></li>
+<li><p>Commit your changes: <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span> <span class="pre">-m</span> <span class="pre">'Add</span> <span class="pre">some</span> <span class="pre">feature'</span></code></p></li>
+<li><p>Don’t forget to fix issues from <code class="docutils literal notranslate"><span class="pre">pre-commit</span></code> pipeline (either add changes made by hooks
+or fix them manually in case of <code class="docutils literal notranslate"><span class="pre">flake8</span></code>)</p></li>
+<li><p>Push to the branch: <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">push</span> <span class="pre">origin</span> <span class="pre">my-new-feature</span></code></p></li>
+<li><p>Submit a pull request. Make sure it is based on the <code class="docutils literal notranslate"><span class="pre">develop</span></code> branch when submitting!
+:D</p></li>
+<li><p>Don’t forget to update the
+<a class="reference external" href="http://moabb.neurotechx.com/docs/whats_new.html">what’s new</a> and
+<a class="reference external" href="http://moabb.neurotechx.com/docs/index.html">documentation</a> pages if needed</p></li>
+</ol>
+</section>
+</section>
+<section id="id4">
+<h2>Setup development environment<a class="headerlink" href="#id4" title="Permalink to this heading">#</a></h2>
+<ol class="arabic simple">
+<li><p>install <code class="docutils literal notranslate"><span class="pre">poetry</span></code> (only once per machine):<code class="docutils literal notranslate"><span class="pre">curl</span> <span class="pre">-sSL</span> <span class="pre">https://install.python-poetry.org</span> <span class="pre">|</span> <span class="pre">python3</span> <span class="pre">-</span></code>or <a class="reference external" href="https://python-poetry.org/docs/#installation">checkout installation instruction</a> or
+use <a class="reference external" href="https://anaconda.org/conda-forge/poetry">conda forge version</a></p></li>
+<li><p>(Optional, skip if not sure) Disable automatic environment creation:<code class="docutils literal notranslate"><span class="pre">poetry</span> <span class="pre">config</span> <span class="pre">virtualenvs.create</span> <span class="pre">false</span></code></p></li>
+<li><p>install all dependencies in one command (have to be run in thibe project directory):<code class="docutils literal notranslate"><span class="pre">poetry</span> <span class="pre">install</span> <span class="pre">--extras</span> <span class="pre">[deeplearning,</span> <span class="pre">codecarbon]</span></code></p></li>
+<li><p>install <code class="docutils literal notranslate"><span class="pre">pre-commit</span></code> hooks to git repo:<code class="docutils literal notranslate"><span class="pre">pre-commit</span> <span class="pre">install</span></code></p></li>
+<li><p>you are ready to code!</p></li>
+</ol>
+<p><em>Note 1:</em>Your first commit will trigger <code class="docutils literal notranslate"><span class="pre">pre-commit</span></code> to download <a class="reference external" href="#tools-used">Code Quality tools</a>.
+That’s OK and it is intended behavior. This will be done once per machine automatically.</p>
+<p><em>Note 2:</em>By default <code class="docutils literal notranslate"><span class="pre">poetry</span></code> creates separate Python virtual environment for every project (<a class="reference external" href="https://python-poetry.org/docs/managing-environments/">more details in documentation</a>).
+If you use <code class="docutils literal notranslate"><span class="pre">conda</span></code> or any other way to manage different environments by hand - you need to
+disable <code class="docutils literal notranslate"><span class="pre">poetry</span></code> environment creation. Also in this case be careful with version of Python
+in your environment - it has to satisfy requirements stated in <code class="docutils literal notranslate"><span class="pre">pyproject.toml</span></code>. In case you
+disable <code class="docutils literal notranslate"><span class="pre">poetry</span></code> you are in charge of this.</p>
+<section id="tools-used">
+<h3>Tools used<a class="headerlink" href="#tools-used" title="Permalink to this heading">#</a></h3>
+<p>MOABB uses <a class="reference external" href="https://python-poetry.org/">poetry</a> for dependency management. This tool
+enables one to have a reproducible environment on all popular OS (Linux, MacOS and
+Windows) and provides easy publishing pipeline.</p>
+<p>Another tool that makes development more stable is <a class="reference external" href="https://pre-commit.com/">pre-commit</a>.
+It automatically runs variety of Code Quality instruments against the code you produced.</p>
+<p>For Code Quality verification, we use:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/psf/black">black</a> - Python code formatting</p></li>
+<li><p><a class="reference external" href="https://github.com/timothycrosley/isort">isort</a> - imports sorting and grouping</p></li>
+<li><p><a class="reference external" href="https://gitlab.com/pycqa/flake8">flake8</a> - code style checking</p></li>
+<li><p><a class="reference external" href="https://github.com/prettier/prettier">prettier</a> - <code class="docutils literal notranslate"><span class="pre">.yml</span></code> and <code class="docutils literal notranslate"><span class="pre">.md</span></code> files formatting</p></li>
+</ul>
+</section>
+<section id="generate-the-documentation">
+<h3>Generate the documentation<a class="headerlink" href="#generate-the-documentation" title="Permalink to this heading">#</a></h3>
+<p>To generate a local version of the documentation:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>docs
+make<span class="w"> </span>html
+</pre></div>
+</div>
+</section>
+</section>
+</section>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#where-to-start">Where to start</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">Code of Conduct</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#contributing-on-github">Contributing on Github</a></li>
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+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-can-i-contribute">How can I contribute?</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#steps-to-contribute">Steps to Contribute</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id4">Setup development environment</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tools-used">Tools used</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#generate-the-documentation">Generate the documentation</a></li>
+</ul>
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+    
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diff --git a/docs/README.html b/docs/README.html
new file mode 100644
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+                      </a>
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+
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+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
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+    <li class="breadcrumb-item active" aria-current="page"><span class="raw-html-m2r"><p align="center">Mother of all BCI Benchmarks</p></span></li>
+  </ul>
+</nav>
+</div>
+      
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+  
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+                  
+  <section id="p-align-center-mother-of-all-bci-benchmarks-p">
+<h1><span class="raw-html-m2r"><p align="center">Mother of all BCI Benchmarks</p></span><a class="headerlink" href="#p-align-center-mother-of-all-bci-benchmarks-p" title="Permalink to this heading">#</a></h1>
+<p align="center" class="large-text">
+  <img src="https://raw.githubusercontent.com/bruAristimunha/moabb/new_theme/docs/source/_static/moabb_logo.svg" width="400" height="400" style="display: block; margin: auto;" />
+  Build a comprehensive benchmark of popular Brain-Computer Interface (BCI) algorithms applied on an extensive list of freely available EEG datasets.
+</p><section id="disclaimer">
+<h2>Disclaimer<a class="headerlink" href="#disclaimer" title="Permalink to this heading">#</a></h2>
+<p><strong>This is an open science project that may evolve depending on the need of the
+community.</strong></p>
+<a class="reference external image-reference" href="https://doi.org/10.5281/zenodo.10034224"><img alt="DOI" src="https://zenodo.org/badge/DOI/10.5281/zenodo.10034224.svg" /></a>
+<a class="reference external image-reference" href="https://github.com/NeuroTechX/moabb/actions?query=branch%3Amaster"><img alt="Build Status" src="https://github.com/NeuroTechX/moabb/workflows/Test/badge.svg" /></a>
+<a class="reference external image-reference" href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg" /></a>
+<a class="reference external image-reference" href="https://codecov.io/gh/NeuroTechX/moabb"><img alt="codecov" src="https://codecov.io/gh/NeuroTechX/moabb/graph/badge.svg?token=NwHD3ethB5" /></a>
+<a class="reference external image-reference" href="https://img.shields.io/pypi/v/moabb"><img alt="PyPI" src="https://img.shields.io/pypi/v/moabb?color=blue&amp;style=plastic" /></a>
+<a class="reference external image-reference" href="https://pepy.tech/project/moabb"><img alt="Downloads" src="https://pepy.tech/badge/moabb" /></a>
+</section>
+<section id="welcome">
+<h2>Welcome!<a class="headerlink" href="#welcome" title="Permalink to this heading">#</a></h2>
+<p>Thank you for visiting the Mother of all BCI Benchmark documentation and associated
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">GitHub repository</a></p>
+<p>This document is a hub to give you some information about the project. Jump straight to
+one of the sections below, or just scroll down to find out more.</p>
+<section id="the-problem">
+<h3>The problem<a class="headerlink" href="#the-problem" title="Permalink to this heading">#</a></h3>
+<p><a class="reference external" href="https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface">Brain-Computer Interfaces</a>
+allow to interact with a computer using brain signals. In this project, we focus mostly on
+electroencephalographic signals
+(<a class="reference external" href="https://en.wikipedia.org/wiki/Electroencephalography">EEG</a>), that is a very active
+research domain, with worldwide scientific contributions. Still:</p>
+<ul class="simple">
+<li><p>Reproducible Research in BCI has a long way to go.</p></li>
+<li><p>While many BCI datasets are made freely available, researchers do not publish code, and
+reproducing results required to benchmark new algorithms turns out to be trickier than
+it should be.</p></li>
+<li><p>Performances can be significantly impacted by parameters of the preprocessing steps,
+toolboxes used and implementation “tricks” that are almost never reported in the
+literature.</p></li>
+</ul>
+<p>As a result, there is no comprehensive benchmark of BCI algorithms, and newcomers are
+spending a tremendous amount of time browsing literature to find out what algorithm works
+best and on which dataset.</p>
+</section>
+<section id="the-solution">
+<h3>The solution<a class="headerlink" href="#the-solution" title="Permalink to this heading">#</a></h3>
+<p>The Mother of all BCI Benchmarks allows to:</p>
+<ul class="simple">
+<li><p>Build a comprehensive benchmark of popular BCI algorithms applied on an extensive list
+of freely available EEG datasets.</p></li>
+<li><p>The code is available on GitHub, serving as a reference point for the future algorithmic
+developments.</p></li>
+<li><p>Algorithms can be ranked and promoted on a website, providing a clear picture of the
+different solutions available in the field.</p></li>
+</ul>
+<p>This project will be successful when we read in an abstract “ … the proposed method
+obtained a score of 89% on the MOABB (Mother of All BCI Benchmarks), outperforming the
+state of the art by 5% …”.</p>
+</section>
+<section id="use-moabb">
+<h3>Use MOABB<a class="headerlink" href="#use-moabb" title="Permalink to this heading">#</a></h3>
+<p>First, you could take a look at our <a class="reference external" href="./auto_tutorials/index.html">tutorials</a> that cover
+the most important concepts and use cases. Also, we have a gallery of
+<a class="reference external" href="./auto_examples/index.html">examples</a> available.</p>
+</section>
+</section>
+<section id="core-team">
+<h2>Core Team<a class="headerlink" href="#core-team" title="Permalink to this heading">#</a></h2>
+<p>This project is under the umbrella of <a class="reference external" href="http://neurotechx.com/">NeuroTechX</a>, the international
+community for NeuroTech enthusiasts.</p>
+<p>The project is currently maintained by:</p>
+<table style="text-align: center;">
+  <thead>
+    <tr>
+      <th>Sylvain Chevallier</th>
+      <th>Bruno Aristimunha</th>
+      <th>Igor Carrara</th>
+      <th>Pierre Guetschel</th>
+      <th>Sara Sedlar</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/5824988?s=150&amp;v=4" alt="Sylvain Chevallier"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/42702466?s=150&amp;v=4" alt="Bruno Aristimunha"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/94047258?s=150&amp;v=4" alt="Igor Carrara"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/25532709?s=150&amp;v=4" alt="Pierre Guetschel"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/5344945?s=150&amp;v=4" alt="Sara Sedlar"></td>
+</tr>
+  </tbody>
+</table><p>The Mother of all BCI Benchmarks was founded by Alexander Barachant and Vinay Jayaram, who
+are experts in the field of Brain-Computer Interfaces (BCI). At moment, both works as
+Research Scientist</p>
+<table style="text-align: center;">
+  <thead>
+    <tr>
+      <th>Alexander Barachant</th>
+      <th>Vinay Jayaram</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td style="padding: 0 15px;"><img src="http://alexandre.barachant.org/images/avatar.jpg" alt="Alexander Barachant" width="150" height="150"></td>
+<td style="padding: 0 15px;"><img src="https://beetl.ai/static/media/vinay.217f36bc.jpeg" alt="Vinay Jayaram" width="150" height="150"></td></tr>
+  </tbody>
+</table></section>
+<section id="contributors">
+<h2>Contributors<a class="headerlink" href="#contributors" title="Permalink to this heading">#</a></h2>
+<p>The MOABB is a community project, and we are always thankful for all the contributors!</p>
+<div id="contributors-container"></div><script>
+const endpoint = 'https://api.github.com/repos/NeuroTechX/moabb/contributors';
+const container = document.getElementById('contributors-container');
+const filterList = ["bruAristimunha", "sylvchev", "carraraig", "pierreGtch", "sara04", "pre-commit-ci[bot]", "dependabot[bot]", "alexandrebarachant", "vinay-jayaram"];
+fetch(endpoint)
+  .then(response => response.json())
+  .then(contributors => {
+    const filteredContributors = contributors.filter(contributor => !filterList.includes(contributor.login));    filteredContributors.forEach(contributor => {
+      const link = document.createElement('a');
+      link.href = contributor.html_url;
+      link.target = '_blank';
+      const img = document.createElement('img');
+      img.src = contributor.avatar_url;
+      img.alt = contributor.login;
+      img.style.width = '100px';
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+      img.style.objectFit = 'cover';
+      img.style.borderRadius = '50%';
+      link.appendChild(img);
+      container.appendChild(link);
+    });
+  });
+</script><div id="contributors-container"></div><p>Special acknowledge for the extra MOABB contributors:</p>
+<table style="text-align: center;">
+  <thead>
+    <tr>
+      <th>Pedro Rodrigues</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td style="padding: 0 15px;"><img src="https://avatars.githubusercontent.com/u/4588557?v=4" alt=" Pedro L. C. Rodrigues" width="150" height="150"></td>
+  </tbody>
+</table><section id="what-do-we-need">
+<h3>What do we need?<a class="headerlink" href="#what-do-we-need" title="Permalink to this heading">#</a></h3>
+<p><strong>You</strong>! In whatever way you can help.</p>
+<p>We need expertise in programming, user experience, software sustainability, documentation
+and technical writing and project management.</p>
+<p>We’d love your feedback along the way.</p>
+<p>Our primary goal is to build a comprehensive benchmark of popular BCI algorithms applied
+on an extensive list of freely available EEG datasets, and we’re excited to support the
+professional development of any and all of our contributors. If you’re looking to learn to
+code, try out working collaboratively, or translate your skills to the digital domain,
+we’re here to help.</p>
+</section>
+</section>
+<section id="citing-moabb-and-related-publications">
+<h2>Citing MOABB and related publications<a class="headerlink" href="#citing-moabb-and-related-publications" title="Permalink to this heading">#</a></h2>
+<p>If you use MOABB in your experiments, please cite this library when
+publishing a paper to increase the visibility of open science initiatives:</p>
+<blockquote>
+<div><p>Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Quentin, B., Schirrmeister, R. T.,Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., Roy, Y., Jayaram, V., Barachant,A., &amp; Chevallier, S.
+Mother of all BCI Benchmarks (MOABB), 2023. DOI: 10.5281/zenodo.10034223.</p>
+</div></blockquote>
+<p>and here is the Bibtex version:</p>
+<blockquote>
+<div><p>&#64;software{Aristimunha_Mother_of_all_2023,
+author = {Aristimunha, Bruno and Carrara, Igor and Guetschel, Pierre and Sedlar, Sara and Rodrigues, Pedro and Sosulski, Jan and Narayanan, Divyesh and Bjareholt, Erik and Quentin, Barthelemy and Schirrmeister, Robin Tibor and Kalunga, Emmanuel and Darmet, Ludovic and Gregoire, Cattan and Abdul Hussain, Ali and Gatti, Ramiro and Goncharenko, Vladislav and Thielen, Jordy and Moreau, Thomas and Roy, Yannick and Jayaram, Vinay and Barachant, Alexandre and Chevallier, Sylvain},
+doi = {10.5281/zenodo.10034223},
+title = {{Mother of all BCI Benchmarks}},
+url = {<a class="github reference external" href="https://github.com/NeuroTechX/moabb">NeuroTechX/moabb</a>},
+version = {1.0.0},
+year = {2023}
+}</p>
+</div></blockquote>
+<p>If you want to cite the scientific contributions of MOABB, you could use the following paper:</p>
+<blockquote>
+<div><p>Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Junqueira Lopes, Sébastien Velut, Salim Khazem, Thomas Moreau
+<a class="reference external" href="https://cnrs.hal.science/hal-04537061/">“The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark”</a>
+HAL: hal-04537061.</p>
+<p>Vinay Jayaram and Alexandre Barachant.
+<a class="reference external" href="http://iopscience.iop.org/article/10.1088/1741-2552/aadea0/meta">“MOABB: trustworthy algorithm benchmarking for BCIs.”</a>
+Journal of neural engineering 15.6 (2018): 066011.
+<a class="reference external" href="https://doi.org/10.1088/1741-2552/aadea0">DOI</a></p>
+</div></blockquote>
+<p>If you publish a paper using MOABB, please contact us on <a class="reference external" href="https://app.gitter.im/#/room/#moabb_dev_community:gitter.im">gitter</a> or open an
+issue, and we will add your paper to the
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/wiki/MOABB-bibliography">dedicated wiki page</a>.</p>
+</section>
+<section id="contact-us">
+<h2>Contact us<a class="headerlink" href="#contact-us" title="Permalink to this heading">#</a></h2>
+<p>If you want to report a problem or suggest an enhancement, we’d love for you to
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues">open an issue</a> at this GitHub repository
+because then we can get right on it.</p>
+<p>For a less formal discussion or exchanging ideas, you can also reach us on the <a class="reference external" href="https://app.gitter.im/#/room/#moabb_dev_community:gitter.im">Gitter
+channel</a> or join our weekly office hours! This an open video meeting
+happening on a <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/191">regular basis</a>, please ask
+the link on the gitter channel. We are also on NeuroTechX Slack channel
+<a class="reference external" href="https://neurotechx.com/">#moabb</a>.</p>
+</section>
+</section>
+
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+  Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.13.3.
+</p></div>
+      
+    </div>
+  
+</div>
+
+  </footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/_downloads/004be3e9d1a7fe3f376ebea76a062f80/4_adding_a_dataset.py b/docs/_downloads/004be3e9d1a7fe3f376ebea76a062f80/4_adding_a_dataset.py
new file mode 100644
index 00000000..43714356
--- /dev/null
+++ b/docs/_downloads/004be3e9d1a7fe3f376ebea76a062f80/4_adding_a_dataset.py
@@ -0,0 +1,174 @@
+"""
+====================================
+Tutorial 4: Creating a dataset class
+====================================
+"""
+
+# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
+#
+# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
+
+import mne
+import numpy as np
+from pyriemann.classification import MDM
+from pyriemann.estimation import Covariances
+from scipy.io import loadmat, savemat
+from sklearn.pipeline import make_pipeline
+
+from moabb.datasets import download as dl
+from moabb.datasets.base import BaseDataset
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+##############################################################################
+# Creating some Data
+# ------------------
+#
+# To illustrate the creation of a dataset class in MOABB, we first create an
+# example dataset saved in .mat file. It contains a single fake recording on
+# 8 channels lasting for 150 seconds (sampling frequency 256 Hz). We have
+# included the script that creates this dataset and have uploaded it online.
+# The fake dataset is available on the
+# `Zenodo website <https://sandbox.zenodo.org/record/369543>`_
+
+
+def create_example_dataset():
+    """Create a fake example for a dataset."""
+    sfreq = 256
+    t_recording = 150
+    t_trial = 1  # duration of a trial
+    intertrial = 2  # time between end of a trial and the next one
+    n_chan = 8
+
+    x = np.zeros((n_chan + 1, t_recording * sfreq))  # electrodes + stimulus
+    stim = np.zeros(t_recording * sfreq)
+    t_offset = 1.0  # offset where the trials start
+    n_trials = 40
+
+    rep = np.linspace(0, 4 * t_trial, t_trial * sfreq)
+    signal = np.sin(2 * np.pi / t_trial * rep)
+    for n in range(n_trials):
+        label = n % 2 + 1  # alternate between class 0 and class 1
+        tn = int(t_offset * sfreq + n * (t_trial + intertrial) * sfreq)
+        stim[tn] = label
+        noise = 0.1 * np.random.randn(n_chan, len(signal))
+        x[:-1, tn : (tn + t_trial * sfreq)] = label * signal + noise
+    x[-1, :] = stim
+    return x, sfreq
+
+
+# Create the fake data
+for subject in [1, 2, 3]:
+    x, fs = create_example_dataset()
+    filename = "subject_" + str(subject).zfill(2) + ".mat"
+    mdict = {}
+    mdict["x"] = x
+    mdict["fs"] = fs
+    savemat(filename, mdict)
+
+##############################################################################
+# Creating a Dataset Class
+# ------------------------
+#
+# We will create now a dataset class using the fake data simulated with the
+# code from above. For this, we first need to import the right classes from
+# MOABB:
+#
+# - ``dl`` is a very useful script that downloads automatically a dataset online
+#   if it is not yet available in the user's computer. The script knows where
+#   to download the files because we create a global variable telling the URL
+#   where to fetch the data.
+# - ``BaseDataset`` is the basic class that we overload to create our dataset.
+#
+# The global variable with the dataset's URL should specify an online
+# repository where all the files are stored.
+
+ExampleDataset_URL = "https://sandbox.zenodo.org/record/369543/files/"
+
+
+##############################################################################
+# The ``ExampleDataset`` needs to implement only 3 functions:
+#
+# - ``__init__`` for indicating the parameter of the dataset
+# - ``_get_single_subject_data`` to define how to process the data once they
+#   have been downloaded
+# - ``data_path`` to define how the data are downloaded.
+
+
+class ExampleDataset(BaseDataset):
+    """Dataset used to exemplify the creation of a dataset class in MOABB.
+
+    The data samples have been simulated and has no physiological
+    meaning whatsoever.
+    """
+
+    def __init__(self):
+        super().__init__(
+            subjects=[1, 2, 3],
+            sessions_per_subject=1,
+            events={"left_hand": 1, "right_hand": 2},
+            code="ExampleDataset",
+            interval=[0, 0.75],
+            paradigm="imagery",
+            doi="",
+        )
+
+    def _get_single_subject_data(self, subject):
+        """Return data for a single subject."""
+        file_path_list = self.data_path(subject)
+
+        data = loadmat(file_path_list[0])
+        x = data["x"]
+        fs = data["fs"]
+        ch_names = ["ch" + str(i) for i in range(8)] + ["stim"]
+        ch_types = ["eeg" for i in range(8)] + ["stim"]
+        info = mne.create_info(ch_names, fs, ch_types)
+        raw = mne.io.RawArray(x, info)
+
+        sessions = {}
+        sessions["0"] = {}
+        sessions["0"]["0"] = raw
+        return sessions
+
+    def data_path(
+        self, subject, path=None, force_update=False, update_path=None, verbose=None
+    ):
+        """Download the data from one subject."""
+        if subject not in self.subject_list:
+            raise (ValueError("Invalid subject number"))
+
+        url = "{:s}subject_0{:d}.mat".format(ExampleDataset_URL, subject)
+        path = dl.data_dl(url, "ExampleDataset")
+        return [path]  # it has to return a list
+
+
+##############################################################################
+# Using the ExampleDataset
+# ------------------------
+#
+# Now that the `ExampleDataset` is defined, it could be instantiated directly.
+# The rest of the code follows the steps described in the previous tutorials.
+
+dataset = ExampleDataset()
+
+paradigm = LeftRightImagery()
+X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[1])
+
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=dataset, overwrite=False, suffix="newdataset"
+)
+pipelines = {}
+pipelines["MDM"] = make_pipeline(Covariances("oas"), MDM(metric="riemann"))
+scores = evaluation.process(pipelines)
+
+print(scores)
+
+##############################################################################
+# Pushing on MOABB Github
+# -----------------------
+#
+# If you want to make your dataset available to everyone, you could upload
+# your data on public server (like Zenodo or Figshare) and signal that you
+# want to add your dataset to MOABB in the  `dedicated issue <https://github.com/NeuroTechX/moabb/issues/1>`_.  # noqa: E501
+# You could then follow the instructions on `how to contribute <https://github.com/NeuroTechX/moabb/blob/master/CONTRIBUTING.md>`_  # noqa: E501
diff --git a/docs/_downloads/0621540f2a81072e792d3fe5436855e1/noplot_phmd_ml_spectrum.ipynb b/docs/_downloads/0621540f2a81072e792d3fe5436855e1/noplot_phmd_ml_spectrum.ipynb
new file mode 100644
index 00000000..74b21d58
--- /dev/null
+++ b/docs/_downloads/0621540f2a81072e792d3fe5436855e1/noplot_phmd_ml_spectrum.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Spectral analysis of the trials\n\nThis example demonstrates how to perform spectral\nanalysis on epochs extracted from a specific subject\nwithin the :class:`moabb.datasets.Cattan2019_PHMD`  dataset.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com>\n# Modified by: Gregoire Cattan <gcattan@hotmail.fr>\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom moabb.datasets import Cattan2019_PHMD\nfrom moabb.paradigms import RestingStateToP300Adapter\n\n\nwarnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n\n1) Specify the channel and subject to compute the power spectrum.\n2) Create an instance of the :class:`moabb.datasets.Cattan2019_PHMD` dataset.\n3) Create an instance of the :class:`moabb.paradigms.RestingStateToP300Adapter`  paradigm.\n   By default, the data is filtered between 1-35 Hz,\n   and epochs are extracted from 10 to 50 seconds after event tagging.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Select channel and subject for the remaining of the example.\nchannel = \"Cz\"\nsubject = 1\n\ndataset = Cattan2019_PHMD()\nevents = [\"on\", \"off\"]\nparadigm = RestingStateToP300Adapter(events=events, channels=[channel])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Estimate Power Spectral Density\n1) Obtain the epochs for the specified subject.\n2) Use Welch's method to estimate the power spectral density.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "f, S, _, y = paradigm.psd(subject, dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Display of the data\n\nPlot the averaged Power Spectral Density (PSD) for each label condition,\nusing the selected channel specified at the beginning of the script.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(facecolor=\"white\", figsize=(8.2, 5.1))\nfor condition in events:\n    mean_power = np.mean(S[y == condition], axis=0).flatten()\n    ax.plot(f, 10 * np.log10(mean_power), label=condition)\n\nax.set_xlim(paradigm.fmin, paradigm.fmax)\nax.set_ylim(100, 135)\nax.set_ylabel(\"Spectrum Magnitude (dB)\", fontsize=14)\nax.set_xlabel(\"Frequency (Hz)\", fontsize=14)\nax.set_title(\"PSD for Channel \" + channel, fontsize=16)\nax.legend()\nfig.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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diff --git a/docs/_downloads/09c597e028c1574e907e103b3db53254/plot_within_session_ssvep.py b/docs/_downloads/09c597e028c1574e907e103b3db53254/plot_within_session_ssvep.py
new file mode 100644
index 00000000..2f136615
--- /dev/null
+++ b/docs/_downloads/09c597e028c1574e907e103b3db53254/plot_within_session_ssvep.py
@@ -0,0 +1,113 @@
+"""
+=====================
+Within Session SSVEP
+=====================
+
+This Example show how to perform a within-session SSVEP analysis on the
+MAMEM dataset 3, using a CCA pipeline.
+
+The within-session evaluation assesses the performance of a classification
+pipeline using a 5-fold cross-validation. The reported metric (here, accuracy)
+is the average of all fold.
+"""
+
+# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
+#
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import MAMEM3
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import SSVEP
+from moabb.pipelines import SSVEP_CCA
+
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+moabb.set_log_level("info")
+
+###############################################################################
+# Loading Dataset
+# ---------------
+#
+# Load 2 subjects of MAMEM3 dataset
+
+subj = [1, 3]
+dataset = MAMEM3()
+dataset.subject_list = subj
+
+###############################################################################
+# Choose Paradigm
+# ---------------
+#
+# We select the paradigm SSVEP, applying a bandpass filter (3-15 Hz) on
+# the data and we keep only the first 3 classes, that is stimulation
+# frequency of 6.66, 7.50 and 8.57 Hz.
+
+paradigm = SSVEP(fmin=3, fmax=15, n_classes=3)
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Use a Canonical Correlation Analysis classifier
+
+interval = dataset.interval
+freqs = paradigm.used_events(dataset)
+
+pipeline = {}
+pipeline["CCA"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=3))
+
+##############################################################################
+# Get Data (optional)
+# -------------------
+#
+# To get access to the EEG signals downloaded from the dataset, you could
+# use `dataset.get_data(subjects=[subject_id])` to obtain the EEG under
+# MNE format, stored in a dictionary of sessions and runs.
+# Otherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])`
+# allows to obtain the EEG data in scikit format, the labels and the meta
+# information. In `paradigm.get_data`, the EEG are preprocessed according
+# to the paradigm requirement.
+
+# sessions = dataset.get_data(subjects=[3])
+# X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[3])
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# The evaluation will return a DataFrame containing a single AUC score for
+# each subject and pipeline.
+
+overwrite = True  # set to True if we want to overwrite cached results
+
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=dataset, suffix="examples", overwrite=overwrite
+)
+results = evaluation.process(pipeline)
+
+print(results.head())
+
+##############################################################################
+# Plot Results
+# ----------------
+#
+# Here we plot the results, indicating the score for each subject
+
+plt.figure()
+sns.barplot(data=results, y="score", x="session", hue="subject", palette="viridis")
+
+##############################################################################
+# And the computation time in seconds
+
+plt.figure()
+ax = sns.barplot(data=results, y="time", x="session", hue="subject", palette="Reds")
+ax.set_ylabel("Time (s)")
+plt.show()
diff --git a/docs/_downloads/11d7e3106c779d2d16f2ad6e45bde649/plot_braindecode.ipynb b/docs/_downloads/11d7e3106c779d2d16f2ad6e45bde649/plot_braindecode.ipynb
new file mode 100644
index 00000000..e71b2d3a
--- /dev/null
+++ b/docs/_downloads/11d7e3106c779d2d16f2ad6e45bde649/plot_braindecode.ipynb
@@ -0,0 +1,133 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cross-session motor imagery with deep learning EEGNet v4 model\nThis example shows how to use BrainDecode in combination with MOABB evaluation.\nIn this example, we use the architecture EEGNetv4.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Igor Carrara <igor.carrara@inria.fr>\n#          Bruno Aristimunha <b.aristimunha@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\nimport mne\nimport seaborn as sns\nimport torch\nfrom braindecode import EEGClassifier\nfrom braindecode.models import EEGNetv4\nfrom sklearn.pipeline import make_pipeline\nfrom skorch.callbacks import EarlyStopping, EpochScoring\nfrom skorch.dataset import ValidSplit\n\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import MotorImagery\nfrom moabb.utils import setup_seed\n\n\nmne.set_log_level(False)\n\n# Print Information PyTorch\nprint(f\"Torch Version: {torch.__version__}\")\n\n# Set up GPU if it is there\ncuda = torch.cuda.is_available()\ndevice = \"cuda\" if cuda else \"cpu\"\nprint(\"GPU is\", \"AVAILABLE\" if cuda else \"NOT AVAILABLE\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use only the dataset ``BNCI2014_001``.\n\n## Running the benchmark\n\nThis example uses the CrossSession evaluation procedure. We focus on the dataset BNCI2014_001 and only on 1 subject\nto reduce computational time.\n\nTo keep the computational time low, the epoch is reduced. In a real situation, we suggest using the following:\nEPOCH = 1000\nPATIENCE = 300\n\nThis code is implemented to run on the CPU. If you're using a GPU, do not use multithreading\n(i.e. set n_jobs=1)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Set random seed to be able to reproduce results\nseed = 42\nsetup_seed(seed)\n\n# Ensure that all operations are deterministic on GPU (if used) for reproducibility\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\n# Hyperparameter\nLEARNING_RATE = 0.0625 * 0.01  # parameter taken from Braindecode\nWEIGHT_DECAY = 0  # parameter taken from Braindecode\nBATCH_SIZE = 64  # parameter taken from BrainDecode\nEPOCH = 10\nPATIENCE = 3\nfmin = 4\nfmax = 100\ntmin = 0\ntmax = None\n\n# Load the dataset\ndataset = BNCI2014_001()\nevents = [\"right_hand\", \"left_hand\"]\nparadigm = MotorImagery(\n    events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax\n)\nsubjects = [1]\nX, _, _ = paradigm.get_data(dataset=dataset, subjects=subjects)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\nIn order to create a pipeline, we need to load a model from braindecode.\nthe second step is to define a skorch model using EEGClassifier from braindecode\nthat allows converting the PyTorch model in a scikit-learn classifier.\nHere, we will use the EEGNet v4 model [1]_ .\nThis model has mandatory hyperparameters (the number of channels, the number of classes,\nand the temporal length of the input) but we do not need to specify them because they will\nbe set dynamically by EEGClassifier using the input data during the call to the ``.fit()`` method.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Define a Skorch classifier\nclf = EEGClassifier(\n    module=EEGNetv4,\n    optimizer=torch.optim.Adam,\n    optimizer__lr=LEARNING_RATE,\n    batch_size=BATCH_SIZE,\n    max_epochs=EPOCH,\n    train_split=ValidSplit(0.2, random_state=seed),\n    device=device,\n    callbacks=[\n        EarlyStopping(monitor=\"valid_loss\", patience=PATIENCE),\n        EpochScoring(\n            scoring=\"accuracy\", on_train=True, name=\"train_acc\", lower_is_better=False\n        ),\n        EpochScoring(\n            scoring=\"accuracy\", on_train=False, name=\"valid_acc\", lower_is_better=False\n        ),\n    ],\n    verbose=1,  # Not printing the results for each epoch\n)\n\n# Create the pipelines\npipes = {}\npipes[\"EEGNetV4\"] = make_pipeline(clf)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset.subject_list = dataset.subject_list[:2]\n\nevaluation = CrossSessionEvaluation(\n    paradigm=paradigm,\n    datasets=dataset,\n    suffix=\"braindecode_example\",\n    overwrite=True,\n    return_epochs=True,\n    n_jobs=1,\n)\n\nresults = evaluation.process(pipes)\n\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "plt.figure()\nsns.barplot(data=results, y=\"score\", x=\"subject\", palette=\"viridis\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n.. [1] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,\n   Hung, C. P., & Lance, B. J. (2018). [EEGNet: a compact convolutional neural\n   network for EEG-based brain-computer interfaces.](https://doi.org/10.1088/1741-2552/aace8c)\n   Journal of neural engineering, 15(5), 056013.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/14eea0f23180ed6c51f0ac41b2f0162e/plot_benchmark_DL.py b/docs/_downloads/14eea0f23180ed6c51f0ac41b2f0162e/plot_benchmark_DL.py
new file mode 100644
index 00000000..032ba81c
--- /dev/null
+++ b/docs/_downloads/14eea0f23180ed6c51f0ac41b2f0162e/plot_benchmark_DL.py
@@ -0,0 +1,128 @@
+"""
+====================================================================
+Benchmarking on MOABB with Tensorflow deep net architectures
+====================================================================
+This example shows how to use MOABB to benchmark a set of Deep Learning pipeline (Tensorflow)
+on all available datasets.
+For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.
+"""
+
+# Authors: Igor Carrara <igor.carrara@inria.fr>
+#
+# License: BSD (3-clause)
+
+import os
+
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from absl.logging import ERROR, set_verbosity
+from tensorflow import keras
+
+from moabb import benchmark, set_log_level
+from moabb.analysis.plotting import score_plot
+from moabb.datasets import BNCI2014_001
+from moabb.utils import setup_seed
+
+
+set_log_level("info")
+# Avoid output Warning
+set_verbosity(ERROR)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Print Information Tensorflow
+print(f"Tensorflow Version: {tf.__version__}")
+print(f"Keras Version: {keras.__version__}")
+
+CPU = len(tf.config.list_physical_devices("CPU")) > 0
+print("CPU is", "AVAILABLE" if CPU else "NOT AVAILABLE")
+
+GPU = len(tf.config.list_physical_devices("GPU")) > 0
+print("GPU is", "AVAILABLE" if GPU else "NOT AVAILABLE")
+
+###############################################################################
+# In this example, we will use only the dataset ``BNCI2014_001``.
+#
+# Running the benchmark
+# ---------------------
+#
+# The benchmark is run using the ``benchmark`` function. You need to specify the
+# folder containing the pipelines to use, the kind of evaluation and the paradigm
+# to use. By default, the benchmark will use all available datasets for all
+# paradigms listed in the pipelines. You could restrict to specific evaluation and
+# paradigm using the ``evaluations`` and ``paradigms`` arguments.
+#
+# To save computation time, the results are cached. If you want to re-run the
+# benchmark, you can set the ``overwrite`` argument to ``True``.
+#
+# It is possible to indicate the folder to cache the results and the one to save
+# the analysis & figures. By default, the results are saved in the ``results``
+# folder, and the analysis & figures are saved in the ``benchmark`` folder.
+#
+# This code is implemented to run on CPU. If you're using a GPU, do not use multithreading
+# (i.e. set n_jobs=1)
+
+# Set up reproducibility of Tensorflow
+setup_seed(42)
+
+# Restrict this example only on the first two subject of BNCI2014_001
+dataset = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:2]
+datasets = [dataset]
+
+results = benchmark(
+    pipelines="./pipelines_DL",
+    evaluations=["WithinSession"],
+    paradigms=["LeftRightImagery"],
+    include_datasets=datasets,
+    results="./results/",
+    overwrite=False,
+    plot=False,
+    output="./benchmark/",
+    n_jobs=-1,
+)
+
+###############################################################################
+# The deep learning architectures implemented in MOABB are:
+# - Shallow Convolutional Network [1]_
+# - Deep Convolutional Network [1]_
+# - EEGNet [2]_
+# - EEGTCNet [3]_
+# - EEGNex [4]_
+# - EEGITNet [5]_
+#
+# Benchmark prints a summary of the results. Detailed results are saved in a
+# pandas dataframe, and can be used to generate figures. The analysis & figures
+# are saved in the ``benchmark`` folder.
+
+score_plot(results)
+plt.show()
+
+##############################################################################
+# References
+# ----------
+# .. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+#    Glasstetter, M., Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).
+#    `Deep learning with convolutional neural networks for EEG decoding and
+#    visualization <https://doi.org/10.1002/hbm.23730>`_.
+#    Human brain mapping, 38(11), 5391-5420.
+# .. [2] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,
+#    Hung, C. P., & Lance, B. J. (2018). `EEGNet: a compact convolutional neural
+#    network for EEG-based brain-computer interfaces.
+#    <https://doi.org/10.1088/1741-2552/aace8c>`_
+#    Journal of neural engineering, 15(5), 056013.
+# .. [3] Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., &
+#    Benini, L. (2020, October). `EEG-TCNet: An accurate temporal convolutional
+#    network for embedded motor-imagery brain-machine interfaces.
+#    <https://doi.org/10.1109/SMC42975.2020.9283028>`_
+#    In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
+#    (pp. 2958-2965). IEEE.
+# .. [4] Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2022). `Toward reliable
+#    signals decoding for electroencephalogram: A benchmark study to EEGNeX.
+#    <https://doi.org/10.48550/arXiv.2207.12369>`_
+#    arXiv preprint arXiv:2207.12369.
+# .. [5] Salami, A., Andreu-Perez, J., & Gillmeister, H. (2022). `EEG-ITNet: An
+#    explainable inception temporal convolutional network for motor imagery
+#    classification
+#    <https://doi.org/10.1109/ACCESS.2022.3161489>`_.
+#    IEEE Access, 10, 36672-36685.
diff --git a/docs/_downloads/1cb0649d22103248d85dfccb151aabae/plot_disk_cache.ipynb b/docs/_downloads/1cb0649d22103248d85dfccb151aabae/plot_disk_cache.ipynb
new file mode 100644
index 00000000..bbc52fad
--- /dev/null
+++ b/docs/_downloads/1cb0649d22103248d85dfccb151aabae/plot_disk_cache.ipynb
@@ -0,0 +1,263 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cache on disk intermediate data processing states\n\nThis example shows how intermediate data processing\nstates can be cached on disk to speed up the loading\nof this data in subsequent calls.\n\nWhen a MOABB paradigm processes a dataset, it will\nfirst apply processing steps to the raw data, this is\ncalled the ``raw_pipeline``. Then, it will convert the\nraw data into epochs and apply processing steps on the\nepochs, this is called the ``epochs_pipeline``.\nFinally, it will eventually convert the epochs into arrays,\nthis is called the ``array_pipeline``. In summary:\n\n``raw_pipeline`` --> ``epochs_pipeline`` --> ``array_pipeline``\n\nAfter each step, MOABB offers the possibility to save on disk\nthe result of the step. This is done by setting the ``cache_config``\nparameter of the paradigm's ``get_data`` method.\nThe ``cache_config`` parameter is a dictionary that can take all\nthe parameters of ``moabb.datasets.base.CacheConfig`` as keys,\nthey are the following: ``use``, ``save_raw``, ``save_epochs``,\n``save_array``, ``overwrite_raw``, ``overwrite_epochs``,\n``overwrite_array``, and ``path``.  You can also directly pass a\n``CacheConfig`` object as ``cache_config``.\n\nIf ``use=False``, the ``save_*`` and ``overwrite_*``\nparameters are ignored.\n\nWhen trying to use the cache (i.e. ``use=True``), MOABB will\nfirst check if there exist a cache of the result of the full\npipeline (i.e. ``raw_pipeline`` --> ``epochs_pipeline`` ->\n``array_pipeline``).\nIf there is none, we remove the last step of the pipeline and\nlook for its cached result. We keep removing steps and looking\nfor a cached result until we find one or until we reach an\nempty pipeline.\nEvery time, if the ``overwrite_*`` parameter\nof the corresponding step is true, we first try to erase the\ncache of this step.\nOnce a cache has been found or the empty pipeline has been reached,\ndepending on the case we either load the cache or the original dataset.\nThen, apply the missing steps one by one and save their result\nif their corresponding ``save_*`` parameter is true.\n\nBy default, only the result of the ``raw_pipeline`` is saved.\nThis is usually a good compromise between speed and disk space\nbecause, when using cached raw data, the epochs can be obtained\nwithout preloading the whole raw signals, only the necessary\nintervals. Yet, because only the raw data is cached, the epoching\nparameters can be changed without creating a new cache each time.\nHowever, if your epoching parameters are fixed, you can directly\ncache the epochs or the arrays to speed up the loading and\nreduce the disk space used.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``cache_config`` parameter is also available for the ``get_data``\n    method of the datasets. It works the same way as for a\n    paradigm except that it will save un-processed raw recordings.</p></div>\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport shutil\nimport tempfile"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import time\nfrom pathlib import Path\n\nfrom moabb import set_log_level\nfrom moabb.datasets import Zhou2016\nfrom moabb.paradigms import LeftRightImagery\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Basic usage\n\nThe ``cache_config`` parameter is a dictionary that has the\nfollowing default values:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "default_cache_config = dict(\n    save_raw=False,\n    save_epochs=False,\n    save_array=False,\n    use=False,\n    overwrite_raw=False,\n    overwrite_epochs=False,\n    overwrite_array=False,\n    path=None,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You don not need to specify all the keys of ``cache_config``, only the ones\nyou want to change.\n\nBy default, the cache is saved at the MNE data directory (i.e. when\n``path=None``).  The MNE data directory can be found with\n``mne.get_config('MNE_DATA')``. For this example, we will save it  in a\ntemporary directory instead:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "temp_dir = Path(tempfile.mkdtemp())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will use the Zhou2016 dataset and the LeftRightImagery paradigm in this\nexample, but this works for any dataset and paradigm pair.:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Zhou2016()\nparadigm = LeftRightImagery()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And we will only use the first subject for this example:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subjects = [1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then, saving a cache can simply be done by setting the desired parameters\nin the ``cache_config`` dictionary:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cache_config = dict(\n    use=True,\n    save_raw=True,\n    save_epochs=True,\n    save_array=True,\n    path=temp_dir,\n)\n_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Time comparison\n\nNow, we will compare the time it takes to load the with different levels of\ncache. For this, we will use the cache saved in the previous block and\noverwrite the steps results one by one so that we can compare the time it\ntakes to load the data and compute the missing steps with an increasing\nnumber of missing steps.\n\nUsing array cache:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cache_config = dict(\n    use=True,\n    path=temp_dir,\n    save_raw=False,\n    save_epochs=False,\n    save_array=False,\n    overwrite_raw=False,\n    overwrite_epochs=False,\n    overwrite_array=False,\n)\nt0 = time.time()\n_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)\nt_array = time.time() - t0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Using epochs cache:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cache_config = dict(\n    use=True,\n    path=temp_dir,\n    save_raw=False,\n    save_epochs=False,\n    save_array=False,\n    overwrite_raw=False,\n    overwrite_epochs=False,\n    overwrite_array=True,\n)\nt0 = time.time()\n_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)\nt_epochs = time.time() - t0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Using raw cache:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cache_config = dict(\n    use=True,\n    path=temp_dir,\n    save_raw=False,\n    save_epochs=False,\n    save_array=False,\n    overwrite_raw=False,\n    overwrite_epochs=True,\n    overwrite_array=True,\n)\nt0 = time.time()\n_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)\nt_raw = time.time() - t0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Using no cache:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cache_config = dict(\n    use=False,\n    path=temp_dir,\n    save_raw=False,\n    save_epochs=False,\n    save_array=False,\n    overwrite_raw=False,\n    overwrite_epochs=False,\n    overwrite_array=False,\n)\nt0 = time.time()\n_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)\nt_nocache = time.time() - t0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Time needed to load the data with different levels of cache:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Using array cache: {t_array:.2f} seconds\")\nprint(f\"Using epochs cache: {t_epochs:.2f} seconds\")\nprint(f\"Using raw cache: {t_raw:.2f} seconds\")\nprint(f\"Without cache: {t_nocache:.2f} seconds\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you can see, using a raw cache is more than 5 times faster than\nwithout cache.\nThis is because when using the raw cache, the data is not preloaded, only\nthe desired epochs are loaded in memory.\n\nUsing the epochs cache is a little faster than the raw cache. This is because\nthere are several preprocessing steps done after the epoching by the\n``epochs_pipeline``. This difference would be greater if the ``resample``\nargument was different that the sampling frequency of the dataset. Indeed,\nthe data loading time is directly proportional to its sampling frequency\nand the resampling is done by the ``epochs_pipeline``.\n\nFinally, we observe very little difference between array and epochs cache.\nThe main interest of the array cache is when the user passes a\ncomputationally heavy but fixed additional preprocessing (for example\ncomputing the covariance matrices of the epochs). This can be done by using\nthe ``postprocess_pipeline`` argument. The output of this additional pipeline\n(necessary a numpy array) will be saved to avoid re-computing it each time.\n\n\n## Technical details\n\nUnder the hood, the cache is saved on disk in a Brain Imaging Data Structure\n(BIDS) compliant format. More details on this structure can be found in the\ntutorial :doc:`./plot_bids_conversion`.\n\nHowever, there are two particular aspects of the way MOABB saves the data\nthat are not specific to BIDS:\n\n* For each file, we set a\n  [description key](https://bids-specification.readthedocs.io/en/stable/appendices/entities.html#desc).\n  This key is a code that corresponds to a hash of the\n  pipeline that was used to generate the data (i.e. from raw to the state\n  of the cache). This code is unique for each different pipeline and allows\n  to identify all the files that were generated by the same pipeline.\n* Once we finish saving all the files for a given combination of dataset,\n  subject, and pipeline, we write a file ending in ``\"_lockfile.json\"`` at\n  the root directory of this subject. This file serves two purposes:\n\n  * It indicates that the cache is complete for this subject and pipeline.\n    If it is not present, it means that something went wrong during the\n    saving process and the cache is incomplete.\n  * The file contains the un-hashed string representation of the pipeline.\n    Therefore, it can be used to identify the pipeline used without having\n    to decode the description key.\n\n## Cleanup\n\nFinally, we can delete the temporary folder:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "shutil.rmtree(temp_dir)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/1e19389137690794e1345c2b750ca301/noplot_bids_conversion.ipynb b/docs/_downloads/1e19389137690794e1345c2b750ca301/noplot_bids_conversion.ipynb
new file mode 100644
index 00000000..54644445
--- /dev/null
+++ b/docs/_downloads/1e19389137690794e1345c2b750ca301/noplot_bids_conversion.ipynb
@@ -0,0 +1,169 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Convert a MOABB dataset to BIDS\n\nThe Brain Imaging Data Structure (BIDS) format\nis standard for storing neuroimaging data.\nIt follows fixed principles to facilitate the\nsharing of neuroimaging data between researchers.\n\nThe MOABB library allows to convert any MOABB dataset to\nBIDS [1]_ and [2]_.\n\nIn this example, we will convert the AlexMI dataset to BIDS using the\noption ``cache_config=dict(path=temp_dir, save_raw=True)`` of the ``get_data``\nmethod from the dataset object.\n\nThis will automatically save the raw data in the BIDS format and allow to use\na cache for the next time the dataset is used.\n\nWe will use the AlexMI dataset [3]_, one of the smallest in\npeople and one that can be downloaded quickly.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport shutil\nimport tempfile\nfrom pathlib import Path\n\nimport mne\n\nfrom moabb import set_log_level\nfrom moabb.datasets import AlexMI\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Basic usage\n\nHere, we will save the BIDS version of the dataset in a temporary folder\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "temp_dir = Path(tempfile.mkdtemp())\n# The conversion of any MOABB dataset to a BIDS-compliant structure can be done\n# by simply calling its ``get_data`` method and using the ``cache_config``\n# parameter. This parameter is a dictionary.\ndataset = AlexMI()\n_ = dataset.get_data(cache_config=dict(path=temp_dir, save_raw=True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Before / after folder structure\n\nTo investigate what was saved, we will first define a function to print\nthe folder structure of a given path:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def print_tree(p: Path, last=True, header=\"\"):\n    elbow = \"\u2514\u2500\u2500\"\n    pipe = \"\u2502  \"\n    tee = \"\u251c\u2500\u2500\"\n    blank = \"   \"\n    print(header + (elbow if last else tee) + p.name)\n    if p.is_dir():\n        children = list(p.iterdir())\n        for i, c in enumerate(children):\n            print_tree(\n                c, header=header + (blank if last else pipe), last=i == len(children) - 1\n            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we will retrieve the location of the original dataset. It is stored\nin the MNE data directory, which can be found with the ``\"MNE_DATA\"`` key:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mne_data = Path(mne.get_config(\"MNE_DATA\"))\nprint(f\"MNE data directory: {mne_data}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we can print the folder structure of the original dataset:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Before conversion:\")\nprint_tree(mne_data / \"MNE-alexeeg-data\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As we can see, before conversion, all the data (i.e. from all subjects,\nsessions and runs) is stored in a single folder. This follows no particular\nstandard and can vary from one dataset to another.\n\nAfter conversion, the data is stored in a BIDS-compliant way:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"After conversion:\")\nprint_tree(temp_dir / \"MNE-BIDS-alexandre-motor-imagery\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the BIDS version of our dataset, the raw files are saved in EDF.\nThe data is organized in a hierarchy of folders,\nstarting with the subjects, then the sessions, and then the runs. Metadata\nfiles are stored to describe the data. For more details on the BIDS\nstructure, please refer to the [BIDS website](https://bids.neuroimaging.io)\nand the [BIDS spec](https://bids-specification.readthedocs.io/en/stable/).\n\nUnder the hood, saving datasets to BIDS is done through the caching system\nof MOABB. Only raw EEG files are officially supported by the BIDS\nspecification.\nHowever, MOABB's caching mechanism also offers the possibility to save\nthe data in a pseudo-BIDS after different preprocessing steps.\nIn particular, we can save :class:`mne.Epochs` and ``np.ndarray`` objects.\nFor more details on the caching system,\nplease refer to the tutorial :doc:`./plot_disk_cache`.\n\n## Cleanup\n\nFinally, we can delete the temporary folder:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "shutil.rmtree(temp_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Pernet, C.R., Appelhoff, S., Gorgolewski, K.J. et al. EEG-BIDS,\n       An extension to the brain imaging data structure for\n       electroencephalography. Sci Data 6, 103 (2019).\n       https://doi.org/10.1038/s41597-019-0104-8\n\n.. [2] Appelhoff et al., (2019). MNE-BIDS: Organizing electrophysiological\n       data into the BIDS format and facilitating their analysis.\n       Journal of Open Source Software, 4(44), 1896,\n       https://doi.org/10.21105/joss.01896\n\n.. [3] Barachant, A., 2012. Commande robuste d'un effecteur par une\n       interface cerveau machine EEG asynchrone (Doctoral dissertation,\n       Universit\u00e9 de Grenoble).\n       https://tel.archives-ouvertes.fr/tel-01196752\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/1eb996e21dc5546952cdf5d77959e6ad/plot_learning_curve_p300_external.py b/docs/_downloads/1eb996e21dc5546952cdf5d77959e6ad/plot_learning_curve_p300_external.py
new file mode 100644
index 00000000..82a1444b
--- /dev/null
+++ b/docs/_downloads/1eb996e21dc5546952cdf5d77959e6ad/plot_learning_curve_p300_external.py
@@ -0,0 +1,149 @@
+"""
+=======================================
+Within Session P300 with Learning Curve
+=======================================
+
+This example shows how to perform a within session analysis while also
+creating learning curves for a P300 dataset.
+Additionally, we will evaluate external code. Make sure to have tdlda installed
+, which can be found in requirements_external.txt
+
+We will compare three pipelines :
+
+- Riemannian geometry
+- Jumping Means-based Linear Discriminant Analysis
+- Time-Decoupled Linear Discriminant Analysis
+
+We will use the P300 paradigm, which uses the AUC as metric.
+"""
+
+# Authors: Jan Sosulski
+#
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+from tdlda import TimeDecoupledLda
+from tdlda import Vectorizer as JumpingMeansVectorizer
+
+import moabb
+from moabb.datasets import BNCI2014_009
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import P300
+
+
+# getting rid of the warnings about the future (on s'en fout !)
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+processing_sampling_rate = 128
+pipelines = {}
+
+# We have to do this because the classes are called 'Target' and 'NonTarget'
+# but the evaluation function uses a LabelEncoder, transforming them
+# to 0 and 1
+labels_dict = {"Target": 1, "NonTarget": 0}
+
+# Riemannian geometry based classification
+pipelines["RG+LDA"] = make_pipeline(
+    XdawnCovariances(nfilter=5, estimator="lwf", xdawn_estimator="scm"),
+    TangentSpace(),
+    LDA(solver="lsqr", shrinkage="auto"),
+)
+
+# Simple LDA pipeline using averaged feature values in certain time intervals
+jumping_mean_ivals = [
+    [0.10, 0.139],
+    [0.14, 0.169],
+    [0.17, 0.199],
+    [0.20, 0.229],
+    [0.23, 0.269],
+    [0.27, 0.299],
+    [0.30, 0.349],
+    [0.35, 0.409],
+    [0.41, 0.449],
+    [0.45, 0.499],
+]
+jmv = JumpingMeansVectorizer(
+    fs=processing_sampling_rate, jumping_mean_ivals=jumping_mean_ivals
+)
+
+pipelines["JM+LDA"] = make_pipeline(jmv, LDA(solver="lsqr", shrinkage="auto"))
+
+# Time-decoupled Covariance classifier, needs information about number of
+# channels and time intervals
+c = TimeDecoupledLda(N_channels=16, N_times=10)
+# TD-LDA needs to know about the used jumping means intervals
+c.preproc = jmv
+pipelines["JM+TD-LDA"] = make_pipeline(jmv, c)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# We define the paradigm (P300) and use the BNCI 2014-009 dataset for it.
+# The evaluation will return a dataframe containing AUCs for each permutation
+# and dataset size.
+
+paradigm = P300(resample=processing_sampling_rate)
+dataset = BNCI2014_009()
+# Remove the slicing of the subject list to evaluate multiple subjects
+dataset.subject_list = dataset.subject_list[0:1]
+datasets = [dataset]
+overwrite = True  # set to True if we want to overwrite cached results
+data_size = dict(policy="ratio", value=np.geomspace(0.02, 1, 6))
+# When the training data is sparse, perform more permutations than when we have
+# a lot of data
+n_perms = np.floor(np.geomspace(20, 2, len(data_size["value"]))).astype(int)
+print(n_perms)
+# Guarantee reproducibility
+np.random.seed(7536298)
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    data_size=data_size,
+    n_perms=n_perms,
+    suffix="examples_lr",
+    overwrite=overwrite,
+)
+
+results = evaluation.process(pipelines)
+
+##############################################################################
+# Plot Results
+# ------------
+#
+# Here we plot the results.
+
+fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])
+
+n_subs = len(dataset.subject_list)
+
+if n_subs > 1:
+    r = results.groupby(["pipeline", "subject", "data_size"]).mean().reset_index()
+else:
+    r = results
+
+sns.pointplot(data=r, x="data_size", y="score", hue="pipeline", ax=ax, palette="Set1")
+
+errbar_meaning = "subjects" if n_subs > 1 else "permutations"
+title_str = f"Errorbar shows Mean-CI across {errbar_meaning}"
+ax.set_xlabel("Amount of training samples")
+ax.set_ylabel("ROC AUC")
+ax.set_title(title_str)
+fig.tight_layout()
+plt.show()
diff --git a/docs/_downloads/1fae02a0d2f8a8b38412328b11979e03/example_codecarbon.py b/docs/_downloads/1fae02a0d2f8a8b38412328b11979e03/example_codecarbon.py
new file mode 100644
index 00000000..eaffaa26
--- /dev/null
+++ b/docs/_downloads/1fae02a0d2f8a8b38412328b11979e03/example_codecarbon.py
@@ -0,0 +1,122 @@
+"""
+=================================================
+Benchmarking with MOABB showing the CO2 footprint
+=================================================
+
+This example shows how to use MOABB to track the CO2 footprint
+using `CodeCarbon library <https://codecarbon.io/>`__.
+For this example, we will use only one
+dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets. Due to limitation of online documentation
+generation, the results is computed on a local cluster but could be easily
+replicated on your infrastructure.
+"""
+
+# Authors: Igor Carrara <igor.carrara@inria.fr>
+#          Bruno Aristimunha <b.aristimunha@gmail.com>
+#
+# License: BSD (3-clause)
+
+###############################################################################
+from moabb import benchmark, set_log_level
+from moabb.analysis.plotting import codecarbon_plot
+from moabb.datasets import BNCI2014_001, Zhou2016
+from moabb.paradigms import LeftRightImagery
+
+
+set_log_level("info")
+
+###############################################################################
+# Loading the pipelines
+# ---------------------
+#
+# To run this example we use several pipelines, ML and DL (Keras) and also
+# pipelines that need an optimization of the hyperparameter.
+# All this different pipelines are stored in ``pipelines_codecarbon``
+
+###############################################################################
+# Selecting the datasets (optional)
+# ---------------------------------
+#
+# If you want to limit your benchmark on a subset of datasets, you can use the
+# ``include_datasets`` and ``exclude_datasets`` arguments. You will need either
+# to provide the dataset's object, or a dataset's code. To get the list of
+# available dataset's code for a given paradigm, you can use the following
+# command:
+
+paradigm = LeftRightImagery()
+for d in paradigm.datasets:
+    print(d.code)
+
+###############################################################################
+# In this example, we will use only the last dataset, 'Zhou 2016', considering
+# only the first subject.
+#
+# Running the benchmark
+# ---------------------
+#
+# The benchmark is run using the ``benchmark`` function. You need to specify the
+# folder containing the pipelines to use, the kind of evaluation and the paradigm
+# to use. By default, the benchmark will use all available datasets for all
+# paradigms listed in the pipelines. You could restrict to specific evaluation
+# and paradigm using the ``evaluations`` and ``paradigms`` arguments.
+#
+# To save computation time, the results are cached. If you want to re-run the
+# benchmark, you can set the ``overwrite`` argument to ``True``.
+#
+# It is possible to indicate the folder to cache the results and the one to
+# save the analysis & figures. By default, the results are saved in the
+# ``results`` folder, and the analysis & figures are saved in the ``benchmark``
+# folder.
+
+dataset = Zhou2016()
+dataset2 = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:1]
+dataset2.subject_list = dataset2.subject_list[:1]
+datasets = [dataset, dataset2]
+
+results = benchmark(
+    pipelines="./pipelines_codecarbon/",
+    evaluations=["WithinSession"],
+    paradigms=["LeftRightImagery"],
+    include_datasets=datasets,
+    results="./results/",
+    overwrite=False,
+    plot=False,
+    output="./benchmark/",
+)
+
+###############################################################################
+# Benchmark prints a summary of the results. Detailed results are saved in a
+# pandas dataframe, and can be used to generate figures. The analysis & figures
+# are saved in the ``benchmark`` folder.
+results.head()
+
+order_list = [
+    "CSP + SVM",
+    "Tangent Space LR",
+    "EN Grid",
+    "CSP + LDA Grid",
+    "Keras_EEGNet_8_2",
+]
+
+###############################################################################
+# Plotting the results
+# --------------------
+# We can plot the results using the ``codecarbon_plot`` function, generated
+# below. This function takes the dataframe returned by the ``benchmark``
+# function as input, and returns a pyplot figure.
+# The ``order_list`` argument is used to specify the order of the pipelines in
+# the plot.
+
+codecarbon_plot(results, order_list, country="(France)")
+
+###############################################################################
+# The result expected will be the following image, but varying depending on the
+# machine and the country used to run the example.
+#
+# .. image:: ../images/example_codecarbon.png
+#    :align: center
+#    :alt: carbon_example
+#
+###############################################################################
diff --git a/docs/_downloads/225d2b2bd0960a1fa6a57be5e1838af9/plot_benchmark.ipynb b/docs/_downloads/225d2b2bd0960a1fa6a57be5e1838af9/plot_benchmark.ipynb
new file mode 100644
index 00000000..5add2d09
--- /dev/null
+++ b/docs/_downloads/225d2b2bd0960a1fa6a57be5e1838af9/plot_benchmark.ipynb
@@ -0,0 +1,126 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Examples of how to use MOABB to benchmark pipelines.\nBenchmarking with MOABB\n=======================\n\nThis example shows how to use MOABB to benchmark a set of pipelines\non all available datasets. For this example, we will use only one\ndataset to keep the computation time low, but this benchmark is designed\nto easily scale to many datasets.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nfrom moabb import benchmark, set_log_level\nfrom moabb.analysis.plotting import score_plot\nfrom moabb.paradigms import LeftRightImagery\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading the pipelines\n\nThe ML pipelines used in benchmark are defined in YAML files, following a\nsimple format. It simplifies sharing and reusing pipelines across benchmarks,\nreproducing state-of-the-art results.\n\nMOABB comes with complete list of pipelines that cover most of the successful\napproaches in the literature. You can find them in the\n[pipelines folder](https://github.com/NeuroTechX/moabb/tree/develop/pipelines).\nFor this example, we will use a folder with only 2 pipelines, to keep the\ncomputation time low.\n\nThis is an example of a pipeline defined in YAML, defining on which paradigms it\ncan be used, the original publication, and the steps to perform using a\nscikit-learn API. In this case, a CSP + SVM pipeline, the covariance are estimated\nto compute a CSP filter and then a linear SVM is trained on the CSP filtered\nsignals.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "with open(\"sample_pipelines/CSP_SVM.yml\", \"r\") as f:\n    lines = f.readlines()\n    for line in lines:\n        print(line, end=\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The ``sample_pipelines`` folder contains a second pipeline, a logistic regression\nperformed in the tangent space using Riemannian geometry.\n\n## Selecting the datasets (optional)\n\nIf you want to limit your benchmark on a subset of datasets, you can use the\n``include_datasets`` and ``exclude_datasets`` arguments. You will need either\nto provide the dataset's object, or a the dataset's code. To get the list of\navailable dataset's code for a given paradigm, you can use the following command:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\nfor d in paradigm.datasets:\n    print(d.code)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use only the last dataset, 'Zhou 2016'.\n\n## Running the benchmark\n\nThe benchmark is run using the ``benchmark`` function. You need to specify the\nfolder containing the pipelines to use, the kind of evaluation and the paradigm\nto use. By default, the benchmark will use all available datasets for all\nparadigms listed in the pipelines. You could restrict to specific evaluation and\nparadigm using the ``evaluations`` and ``paradigms`` arguments.\n\nTo save computation time, the results are cached. If you want to re-run the\nbenchmark, you can set the ``overwrite`` argument to ``True``.\n\nIt is possible to indicate the folder to cache the results and the one to save\nthe analysis & figures. By default, the results are saved in the ``results``\nfolder, and the analysis & figures are saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results = benchmark(\n    pipelines=\"./sample_pipelines/\",\n    evaluations=[\"WithinSession\"],\n    paradigms=[\"LeftRightImagery\"],\n    include_datasets=[\"Zhou2016\"],\n    results=\"./results/\",\n    overwrite=False,\n    plot=False,\n    output=\"./benchmark/\",\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Benchmark prints a summary of the results. Detailed results are saved in a\npandas dataframe, and can be used to generate figures. The analysis & figures\nare saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "score_plot(results)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/284aff1e2748c45a1efdc5d7071c4a77/plot_benchmark_braindecode.py b/docs/_downloads/284aff1e2748c45a1efdc5d7071c4a77/plot_benchmark_braindecode.py
new file mode 100644
index 00000000..3b69b56b
--- /dev/null
+++ b/docs/_downloads/284aff1e2748c45a1efdc5d7071c4a77/plot_benchmark_braindecode.py
@@ -0,0 +1,122 @@
+"""
+=======================================================================
+Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures
+=======================================================================
+This example shows how to use MOABB to benchmark a set of Braindecode pipelines (deep learning
+architectures) on all available datasets.
+For this example, we will use only 2 datasets to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.
+"""
+
+# Authors: Igor Carrara <igor.carrara@inria.fr>
+#          Bruno Aristimunha <b.aristimunha@gmail.com>
+#          Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>
+#
+# License: BSD (3-clause)
+
+import os
+
+import matplotlib.pyplot as plt
+import torch
+from absl.logging import ERROR, set_verbosity
+
+from moabb import benchmark, set_log_level
+from moabb.analysis.plotting import score_plot
+from moabb.datasets import BNCI2014_001, BNCI2014_004
+from moabb.utils import setup_seed
+
+
+set_log_level("info")
+# Avoid output Warning
+set_verbosity(ERROR)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Print Information PyTorch
+print(f"Torch Version: {torch.__version__}")
+
+# Set up GPU if it is there
+cuda = torch.cuda.is_available()
+device = "cuda" if cuda else "cpu"
+print("GPU is", "AVAILABLE" if cuda else "NOT AVAILABLE")
+
+###############################################################################
+# In this example, we will use only 2 subjects from the dataset ``BNCI2014_001`` and ``BNCI2014_004``.
+#
+# Running the benchmark
+# ---------------------
+#
+# The benchmark is run using the ``benchmark`` function. You need to specify the
+# folder containing the pipelines, the kind of evaluation, and the paradigm
+# to use. By default, the benchmark will use all available datasets for all
+# paradigms listed in the pipelines. You could restrict to specific evaluation and
+# paradigm using the ``evaluations`` and ``paradigms`` arguments.
+#
+# To save computation time, the results are cached. If you want to re-run the
+# benchmark, you can set the ``overwrite`` argument to ``True``.
+#
+# It is possible to indicate the folder to cache the results and the one to save
+# the analysis & figures. By default, the results are saved in the ``results``
+# folder, and the analysis & figures are saved in the ``benchmark`` folder.
+#
+# This code is implemented to run on CPU. If you're using a GPU, do not use multithreading
+# (i.e. set n_jobs=1)
+#
+# In order to allow the benchmark function to work with return_epoch=True (Required to use Braindecode(
+# we need to call each pipeline as "braindecode_xxx...", with xxx the name of the model to be
+# handled correctly by the benchmark function.
+
+# Set up reproducibility of Tensorflow
+setup_seed(42)
+
+# Restrict this example only to the first two subjects of BNCI2014_001
+dataset = BNCI2014_001()
+dataset2 = BNCI2014_004()
+dataset.subject_list = dataset.subject_list[:2]
+dataset2.subject_list = dataset2.subject_list[:2]
+datasets = [dataset, dataset2]
+
+results = benchmark(
+    pipelines="./pipelines_braindecode",
+    evaluations=["CrossSession"],
+    paradigms=["LeftRightImagery"],
+    include_datasets=datasets,
+    results="./results/",
+    overwrite=False,
+    plot=False,
+    output="./benchmark/",
+    n_jobs=-1,
+)
+
+###############################################################################
+# The deep learning architectures implemented in MOABB using Braindecode are:
+#
+# - Shallow Convolutional Network [1]_
+# - Deep Convolutional Network [1]_
+# - EEGNetv4 [2]_
+# - EEGInception [3]_
+#
+# Benchmark prints a summary of the results. Detailed results are saved in a
+# pandas dataframe, and can be used to generate figures. The analysis & figures
+# are saved in the ``benchmark`` folder.
+
+score_plot(results)
+plt.show()
+
+##############################################################################
+# References
+# ----------
+# .. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+#    Glasstetter, M., Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).
+#    `Deep learning with convolutional neural networks for EEG decoding and
+#    visualization <https://doi.org/10.1002/hbm.23730>`_.
+#    Human brain mapping, 38(11), 5391-5420.
+# .. [2] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,
+#    Hung, C. P., & Lance, B. J. (2018). `EEGNet: a compact convolutional neural
+#    network for EEG-based brain-computer interfaces.
+#    <https://doi.org/10.1088/1741-2552/aace8c>`_
+#    Journal of neural engineering, 15(5), 056013.
+# .. [3] Santamaria-Vazquez, E., Martinez-Cagigal, V., Vaquerizo-Villar,
+#    F., & Hornero, R. (2020). `EEG-inception: A novel deep convolutional neural network
+#    for assistive ERP-based brain-computer interfaces.
+#    <https://doi.org/10.1109/TNSRE.2020.3048106>`_
+#    IEEE Transactions on Neural Systems and Rehabilitation Engineering
diff --git a/docs/_downloads/2a0ca02a0f1b91ece5afbc9bdeceffe4/plot_fixed_interval_windows.ipynb b/docs/_downloads/2a0ca02a0f1b91ece5afbc9bdeceffe4/plot_fixed_interval_windows.ipynb
new file mode 100644
index 00000000..56447cef
--- /dev/null
+++ b/docs/_downloads/2a0ca02a0f1b91ece5afbc9bdeceffe4/plot_fixed_interval_windows.ipynb
@@ -0,0 +1,180 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Fixed interval windows processing\n\nThis example shows how to process a dataset using the\n:class:`moabb.paradigms.FixedIntervalWindowsProcessing` paradigm. This paradigm\ncreates epochs at fixed intervals, ignoring the stim\nchannel and events of the datasets. Therefore, it is\ncompatible with all the datasets. Unfortunately,\nthis paradigm is not compatible with the MOABB evaluation\nframework. However, it can be used to process datasets\nfor unsupervised algorithms.\n\nIn this example, we will use the Zhou2016 dataset because\nit is relatively small and can be downloaded quickly.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport mne\nimport numpy as np\n\nfrom moabb import set_log_level\nfrom moabb.datasets import Zhou2016\nfrom moabb.paradigms import FixedIntervalWindowsProcessing, MotorImagery\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Process a dataset\n\nTo process a dataset with\n:class:`moabb.paradigms.FixedIntervalWindowsProcessing` , you can use the\nmethod as with every other paradigm. The only additional parameters are\n``length``, ``stride``, ``start_offset``, and ``stop_offset``. They are\nall parametrised in seconds. ``length`` is the length of the epochs,\n``stride`` is the time between the onset of two consecutive epochs,\n``start_offset`` is the offset between each run start and their first\nepoch, and ``stop_offset`` is the offset between each run start and their\nlast epoch. The default values are ``length=5``, ``stride=10``,\n``start_offset=0``, and ``stop_offset=None`` (i.e. end of the run).\n\nAn example usage of :class:`moabb.paradigms.FixedIntervalWindowsProcessing`\nwith the :class:`moabb.datasets.Zhou2016` dataset:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Zhou2016()\nprocessing = FixedIntervalWindowsProcessing(\n    # new parameters:\n    length=100,\n    stride=50,\n    start_offset=300,\n    stop_offset=900,  # we epoch 10 minutes per run, starting at 5 minutes (i.e. 300 seconds)\n    # parameters common with other paradigms:\n    resample=100,\n    fmin=7,\n    fmax=45,\n    baseline=None,\n    channels=None,\n)\nX, labels, metadata = processing.get_data(dataset=dataset, subjects=[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this dataset, there are three sessions per subject and two runs per\nsession:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for column in metadata.columns:\n    print(f\"{column}s: {metadata[column].unique()}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We expect to obtained ``(stop_offset - start_offset - length) / stride``;\ni.e. $(900-300-100)/50=10$ epochs per run. Here we have 3*2=6 runs.\nAnd indeed, we obtain\na total of $6*10=60$ epochs:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Number of epochs: {len(X)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>To apply a bank of bandpass filters, you can use the\n    :class:`moabb.paradigms.FilterBankFixedIntervalWindowsProcessing`\n    paradigm instead.</p></div>\n\n## Print the events\n\nWe can print the position of the created epochs within the run next to\nthe original events of the dataset. For this, we will first instantiate\na :class:`moabb.paradigms.MotorImagery` paradigm to recover the original\nevents of the dataset:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = MotorImagery(\n    resample=100,\n    fmin=7,\n    fmax=45,\n    baseline=None,\n    channels=None,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then, we can recover the events of both paradigms using the\n``_get_events_pipeline`` method:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "events_pipeline_dataset = paradigm._get_events_pipeline(dataset)\nevents_pipeline_fixed = processing._get_events_pipeline(dataset)\nraw = dataset.get_data(subjects=[1])[1][\"0\"][\"0\"]\nevents_dataset = events_pipeline_dataset.transform(raw)\nevents_fixed = events_pipeline_fixed.transform(raw)\nevents = np.concatenate([events_dataset, events_fixed])\nevent_id = dict(**paradigm.used_events(dataset), **processing.used_events(dataset))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can plot the events. The artificial events created by\n:class:`moabb.paradigms.FixedIntervalWindowsProcessing` are named\n``\"Windows\"``:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = mne.viz.plot_events(\n    events,\n    sfreq=raw.info[\"sfreq\"],\n    event_id=event_id,\n)\nfig.subplots_adjust(right=0.7)\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the epochs were effectively created at a fixed interval\nevery 50 seconds between 300 and 900 seconds, and ignoring\nthe original events of the dataset.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/321fdb18266d890de5e5f577f03c2c73/plot_within_session_p300.ipynb b/docs/_downloads/321fdb18266d890de5e5f577f03c2c73/plot_within_session_p300.ipynb
new file mode 100644
index 00000000..e10a3c3e
--- /dev/null
+++ b/docs/_downloads/321fdb18266d890de5e5f577f03c2c73/plot_within_session_p300.ipynb
@@ -0,0 +1,144 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Within Session P300\n\nThis example shows how to perform a within session analysis on three different\nP300 datasets.\n\nWe will compare two pipelines :\n\n- Riemannian geometry\n- XDAWN with Linear Discriminant Analysis\n\nWe will use the P300 paradigm, which uses the AUC as metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom mne.decoding import Vectorizer\nfrom pyriemann.estimation import Xdawn, XdawnCovariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_009\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import P300"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "getting rid of the warnings about the future\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\n\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have to do this because the classes are called 'Target' and 'NonTarget'\nbut the evaluation function uses a LabelEncoder, transforming them\nto 0 and 1\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "labels_dict = {\"Target\": 1, \"NonTarget\": 0}\n\npipelines[\"RG+LDA\"] = make_pipeline(\n    XdawnCovariances(\n        nfilter=2, classes=[labels_dict[\"Target\"]], estimator=\"lwf\", xdawn_estimator=\"scm\"\n    ),\n    TangentSpace(),\n    LDA(solver=\"lsqr\", shrinkage=\"auto\"),\n)\n\npipelines[\"Xdw+LDA\"] = make_pipeline(\n    Xdawn(nfilter=2, estimator=\"scm\"), Vectorizer(), LDA(solver=\"lsqr\", shrinkage=\"auto\")\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nWe define the paradigm (P300) and use all three datasets available for it.\nThe evaluation will return a DataFrame containing a single AUC score for\neach subject / session of the dataset, and for each pipeline.\n\nResults are saved into the database, so that if you add a new pipeline, it\nwill not run again the evaluation unless a parameter has changed. Results can\nbe overwritten if necessary.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = P300(resample=128)\ndataset = BNCI2014_009()\ndataset.subject_list = dataset.subject_list[:2]\ndatasets = [dataset]\noverwrite = True  # set to True if we want to overwrite cached results\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"examples\", overwrite=overwrite\n)\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we plot the results to compare the two pipelines\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(facecolor=\"white\", figsize=[8, 4])\n\nsns.stripplot(\n    data=results,\n    y=\"score\",\n    x=\"pipeline\",\n    ax=ax,\n    jitter=True,\n    alpha=0.5,\n    zorder=1,\n    palette=\"Set1\",\n)\nsns.pointplot(data=results, y=\"score\", x=\"pipeline\", ax=ax, palette=\"Set1\")\n\nax.set_ylabel(\"ROC AUC\")\nax.set_ylim(0.5, 1)\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/33bee7d7204a7cb56293bd1d838b63f8/plot_statistical_analysis.ipynb b/docs/_downloads/33bee7d7204a7cb56293bd1d838b63f8/plot_statistical_analysis.ipynb
new file mode 100644
index 00000000..eb692ea3
--- /dev/null
+++ b/docs/_downloads/33bee7d7204a7cb56293bd1d838b63f8/plot_statistical_analysis.ipynb
@@ -0,0 +1,180 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Statistical Analysis\n\nThe MOABB codebase comes with convenience plotting utilities and some\nstatistical testing. This tutorial focuses on what those exactly are and how\nthey can be used.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Vinay Jayaram <vinayjayaram13@gmail.com>\n#\n# License: BSD (3-clause)\n# sphinx_gallery_thumbnail_number = -2\n\nimport matplotlib.pyplot as plt\nfrom mne.decoding import CSP\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nimport moabb.analysis.plotting as moabb_plt\nfrom moabb.analysis.meta_analysis import (  # noqa: E501\n    compute_dataset_statistics,\n    find_significant_differences,\n)\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nmoabb.set_log_level(\"info\")\n\nprint(__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results Generation\n\nFirst we need to set up a paradigm, dataset list, and some pipelines to\ntest. This is explored more in the examples -- we choose left vs right\nimagery paradigm with a single bandpass. There is only one dataset here but\nany number can be added without changing this workflow.\n\n## Create Pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\n\nThe CSP implementation from MNE is used. We selected 8 CSP components, as\nusually done in the literature.\n\nThe Riemannian geometry pipeline consists in covariance estimation, tangent\nspace mapping and finally a logistic regression for the classification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\n\npipelines[\"CSP+LDA\"] = make_pipeline(CSP(n_components=8), LDA())\n\npipelines[\"RG+LR\"] = make_pipeline(Covariances(), TangentSpace(), LogisticRegression())\n\npipelines[\"CSP+LR\"] = make_pipeline(CSP(n_components=8), LogisticRegression())\n\npipelines[\"RG+LDA\"] = make_pipeline(Covariances(), TangentSpace(), LDA())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nWe define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).\nThe evaluation will return a DataFrame containing a single AUC score for\neach subject / session of the dataset, and for each pipeline.\n\nResults are saved into the database, so that if you add a new pipeline, it\nwill not run again the evaluation unless a parameter has changed. Results can\nbe overwritten if necessary.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\ndataset = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:4]\ndatasets = [dataset]\noverwrite = True  # set to False if we want to use cached results\nevaluation = CrossSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"stats\", overwrite=overwrite\n)\n\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## MOABB Plotting\n\nHere we plot the results using some of the convenience methods within the\ntoolkit.  The score_plot visualizes all the data with one score per subject\nfor every dataset and pipeline.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = moabb_plt.score_plot(results)\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For a comparison of two algorithms, there is the paired_plot, which plots\nperformance in one versus the performance in the other over all chosen\ndatasets. Note that there is only one score per subject, regardless of the\nnumber of sessions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = moabb_plt.paired_plot(results, \"CSP+LDA\", \"RG+LDA\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Statistical Testing and Further Plots\n\nIf the statistical significance of results is of interest, the method\ncompute_dataset_statistics allows one to show a meta-analysis style plot as\nwell. For an overview of how all algorithms perform in comparison with each\nother, the method find_significant_differences and the summary_plot are\npossible.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "stats = compute_dataset_statistics(results)\nP, T = find_significant_differences(stats)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The meta-analysis style plot shows the standardized mean difference within\neach tested dataset for the two algorithms in question, in addition to a\nmeta-effect and significance both per-dataset and overall.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = moabb_plt.meta_analysis_plot(stats, \"CSP+LDA\", \"RG+LDA\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The summary plot shows the effect and significance related to the hypothesis\nthat the algorithm on the y-axis significantly outperformed the algorithm on\nthe x-axis over all datasets\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "moabb_plt.summary_plot(P, T)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/3492da53de2fcbbc267c5e45e1d04fb9/plot_learning_curve_p300.ipynb b/docs/_downloads/3492da53de2fcbbc267c5e45e1d04fb9/plot_learning_curve_p300.ipynb
new file mode 100644
index 00000000..f07388ab
--- /dev/null
+++ b/docs/_downloads/3492da53de2fcbbc267c5e45e1d04fb9/plot_learning_curve_p300.ipynb
@@ -0,0 +1,126 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Within Session P300 with Learning Curve\n\nThis example shows how to perform a within session analysis while also\ncreating learning curves for a P300 dataset.\nAdditionally, we will evaluate external code. Make sure to have tdlda installed, which\ncan be found in requirements_external.txt\n\nWe will compare two pipelines :\n\n- Riemannian geometry with Linear Discriminant Analysis\n- XDAWN and Linear Discriminant Analysis\n\nWe will use the P300 paradigm, which uses the AUC as metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Jan Sosulski\n#\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom mne.decoding import Vectorizer\nfrom pyriemann.estimation import XdawnCovariances\nfrom pyriemann.spatialfilters import Xdawn\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_009\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import P300\n\n\n# getting rid of the warnings about the future (on s'en fout !)\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\n\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "processing_sampling_rate = 128\npipelines = {}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have to do this because the classes are called 'Target' and 'NonTarget'\nbut the evaluation function uses a LabelEncoder, transforming them\nto 0 and 1\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "labels_dict = {\"Target\": 1, \"NonTarget\": 0}\n\n# Riemannian geometry based classification\npipelines[\"RG+LDA\"] = make_pipeline(\n    XdawnCovariances(nfilter=5, estimator=\"lwf\", xdawn_estimator=\"scm\"),\n    TangentSpace(),\n    LDA(solver=\"lsqr\", shrinkage=\"auto\"),\n)\n\npipelines[\"Xdw+LDA\"] = make_pipeline(\n    Xdawn(nfilter=2, estimator=\"scm\"), Vectorizer(), LDA(solver=\"lsqr\", shrinkage=\"auto\")\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nWe define the paradigm (P300) and use all three datasets available for it.\nThe evaluation will return a DataFrame containing AUCs for each permutation\nand dataset size.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = P300(resample=processing_sampling_rate)\ndataset = BNCI2014_009()\n# Remove the slicing of the subject list to evaluate multiple subjects\ndataset.subject_list = dataset.subject_list[1:2]\ndatasets = [dataset]\noverwrite = True  # set to True if we want to overwrite cached results\ndata_size = dict(policy=\"ratio\", value=np.geomspace(0.02, 1, 4))\n# When the training data is sparse, perform more permutations than when we have a lot of data\nn_perms = np.floor(np.geomspace(20, 2, len(data_size[\"value\"]))).astype(int)\n# Guarantee reproducibility\nnp.random.seed(7536298)\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm,\n    datasets=datasets,\n    data_size=data_size,\n    n_perms=n_perms,\n    suffix=\"examples_lr\",\n    overwrite=overwrite,\n)\n\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nWe plot the accuracy as a function of the number of training samples, for\neach pipeline\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(facecolor=\"white\", figsize=[8, 4])\n\nn_subs = len(dataset.subject_list)\n\nif n_subs > 1:\n    r = results.groupby([\"pipeline\", \"subject\", \"data_size\"]).mean().reset_index()\nelse:\n    r = results\n\nsns.pointplot(data=r, x=\"data_size\", y=\"score\", hue=\"pipeline\", ax=ax, palette=\"Set1\")\n\nerrbar_meaning = \"subjects\" if n_subs > 1 else \"permutations\"\ntitle_str = f\"Errorbar shows Mean-CI across {errbar_meaning}\"\nax.set_xlabel(\"Amount of training samples\")\nax.set_ylabel(\"ROC AUC\")\nax.set_title(title_str)\nfig.tight_layout()\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/38abfe5b8b42dcb9de08824fd90d346b/plot_cross_subject_ssvep.ipynb b/docs/_downloads/38abfe5b8b42dcb9de08824fd90d346b/plot_cross_subject_ssvep.ipynb
new file mode 100644
index 00000000..5814e615
--- /dev/null
+++ b/docs/_downloads/38abfe5b8b42dcb9de08824fd90d346b/plot_cross_subject_ssvep.ipynb
@@ -0,0 +1,234 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cross-Subject SSVEP\nThis example shows how to perform a cross-subject analysis on an SSVEP dataset.\nWe will compare four pipelines :\n\n- Riemannian Geometry\n- CCA\n- TRCA\n- MsetCCA\n\nWe will use the SSVEP paradigm, which uses the AUC as metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>\n#\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import Kalunga2016\nfrom moabb.evaluations import CrossSubjectEvaluation\nfrom moabb.paradigms import SSVEP, FilterBankSSVEP\nfrom moabb.pipelines import SSVEP_CCA, SSVEP_TRCA, ExtendedSSVEPSignal, SSVEP_MsetCCA\n\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading Dataset\n\nWe will load the data from the first 2 subjects of the ``SSVEP_Exo`` dataset\nand compare two algorithms on this set. One of the algorithms could only\nprocess class associated with a stimulation frequency, we will thus drop\nthe resting class. As the resting class is the last defined class, picking\nthe first three classes (out of four) allows to focus only on the stimulation\nfrequency.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_subject = 2\ndataset = Kalunga2016()\ndataset.subject_list = dataset.subject_list[:n_subject]\ninterval = dataset.interval"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Choose Paradigm\n\nWe define the paradigms (SSVEP, SSVEP TRCA, SSVEP MsetCCA, and FilterBankSSVEP) and\nuse the dataset Kalunga2016. All 3 SSVEP paradigms applied a bandpass filter (10-42 Hz) on\nthe data, which include all stimuli frequencies and their first harmonics,\nwhile the FilterBankSSVEP paradigm uses as many bandpass filters as\nthere are stimulation frequencies (here 3). For each stimulation frequency\nthe EEG is filtered with a 1 Hz-wide bandpass filter centered on the\nfrequency. This results in ``n_classes`` copies of the signal, filtered for each\nclass, as used in the filterbank motor imagery paradigms.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = SSVEP(fmin=10, fmax=42, n_classes=3)\nparadigm_TRCA = SSVEP(fmin=10, fmax=42, n_classes=3)\nparadigm_MSET_CCA = SSVEP(fmin=10, fmax=42, n_classes=3)\nparadigm_fb = FilterBankSSVEP(filters=None, n_classes=3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Classes are defined by the frequency of the stimulation, here we use\nthe first two frequencies of the dataset, 13 and 17 Hz.\nThe evaluation function uses a LabelEncoder, transforming them\nto 0 and 1\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "freqs = paradigm.used_events(dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\nThe first pipeline uses Riemannian geometry, by building an extended\ncovariance matrices from the signal filtered around the considered\nfrequency and applying a logistic regression in the tangent plane.\nThe second pipeline relies on the above defined CCA classifier.\nThe third pipeline relies on the TRCA algorithm,\nand the fourth uses the MsetCCA algorithm. Both CCA based methods\n(i.e. CCA and MsetCCA) used 3 CCA components.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines_fb = {}\npipelines_fb[\"RG+LogReg\"] = make_pipeline(\n    ExtendedSSVEPSignal(),\n    Covariances(estimator=\"lwf\"),\n    TangentSpace(),\n    LogisticRegression(solver=\"lbfgs\", multi_class=\"auto\"),\n)\n\npipelines = {}\npipelines[\"CCA\"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=2))\n\npipelines_TRCA = {}\npipelines_TRCA[\"TRCA\"] = make_pipeline(SSVEP_TRCA(interval=interval, freqs=freqs))\n\npipelines_MSET_CCA = {}\npipelines_MSET_CCA[\"MSET_CCA\"] = make_pipeline(SSVEP_MsetCCA(freqs=freqs))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nThe evaluation will return a DataFrame containing an accuracy score for\neach subject / session of the dataset, and for each pipeline.\n\nResults are saved into the database, so that if you add a new pipeline, it\nwill not run again the evaluation unless a parameter has changed. Results can\nbe overwritten if necessary.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "overwrite = False  # set to True if we want to overwrite cached results\n\nevaluation = CrossSubjectEvaluation(\n    paradigm=paradigm, datasets=dataset, overwrite=overwrite\n)\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Filter bank processing, determine the filter automatically from the\nstimulation frequency values of events.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "evaluation_fb = CrossSubjectEvaluation(\n    paradigm=paradigm_fb, datasets=dataset, overwrite=overwrite\n)\nresults_fb = evaluation_fb.process(pipelines_fb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "TRCA processing also relies on filter bank that is automatically designed.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "evaluation_TRCA = CrossSubjectEvaluation(\n    paradigm=paradigm_TRCA, datasets=dataset, overwrite=overwrite\n)\nresults_TRCA = evaluation_TRCA.process(pipelines_TRCA)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "MsetCCA processing\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "evaluation_MSET_CCA = CrossSubjectEvaluation(\n    paradigm=paradigm_MSET_CCA, datasets=dataset, overwrite=overwrite\n)\nresults_MSET_CCA = evaluation_MSET_CCA.process(pipelines_MSET_CCA)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After processing the four, we simply concatenate the results.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results = pd.concat([results, results_fb, results_TRCA, results_MSET_CCA])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we display the results as stripplot, with a pointplot for error bar.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(facecolor=\"white\", figsize=[8, 4])\nsns.stripplot(\n    data=results,\n    y=\"score\",\n    x=\"pipeline\",\n    ax=ax,\n    jitter=True,\n    alpha=0.5,\n    zorder=1,\n    palette=\"Set1\",\n)\nsns.pointplot(data=results, y=\"score\", x=\"pipeline\", ax=ax, palette=\"Set1\")\nax.set_ylabel(\"Accuracy\")\nax.set_ylim(0.1, 0.6)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/39844aaaee383c2803c6ac93e64037d5/plot_within_session_p300.py b/docs/_downloads/39844aaaee383c2803c6ac93e64037d5/plot_within_session_p300.py
new file mode 100644
index 00000000..5939879d
--- /dev/null
+++ b/docs/_downloads/39844aaaee383c2803c6ac93e64037d5/plot_within_session_p300.py
@@ -0,0 +1,117 @@
+"""
+===========================
+Within Session P300
+===========================
+
+This example shows how to perform a within session analysis on three different
+P300 datasets.
+
+We will compare two pipelines :
+
+- Riemannian geometry
+- XDAWN with Linear Discriminant Analysis
+
+We will use the P300 paradigm, which uses the AUC as metric.
+
+"""
+
+# Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com>
+#
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from mne.decoding import Vectorizer
+from pyriemann.estimation import Xdawn, XdawnCovariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_009
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import P300
+
+
+##############################################################################
+# getting rid of the warnings about the future
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+
+
+pipelines = {}
+
+##############################################################################
+# We have to do this because the classes are called 'Target' and 'NonTarget'
+# but the evaluation function uses a LabelEncoder, transforming them
+# to 0 and 1
+labels_dict = {"Target": 1, "NonTarget": 0}
+
+pipelines["RG+LDA"] = make_pipeline(
+    XdawnCovariances(
+        nfilter=2, classes=[labels_dict["Target"]], estimator="lwf", xdawn_estimator="scm"
+    ),
+    TangentSpace(),
+    LDA(solver="lsqr", shrinkage="auto"),
+)
+
+pipelines["Xdw+LDA"] = make_pipeline(
+    Xdawn(nfilter=2, estimator="scm"), Vectorizer(), LDA(solver="lsqr", shrinkage="auto")
+)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# We define the paradigm (P300) and use all three datasets available for it.
+# The evaluation will return a DataFrame containing a single AUC score for
+# each subject / session of the dataset, and for each pipeline.
+#
+# Results are saved into the database, so that if you add a new pipeline, it
+# will not run again the evaluation unless a parameter has changed. Results can
+# be overwritten if necessary.
+
+paradigm = P300(resample=128)
+dataset = BNCI2014_009()
+dataset.subject_list = dataset.subject_list[:2]
+datasets = [dataset]
+overwrite = True  # set to True if we want to overwrite cached results
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
+)
+results = evaluation.process(pipelines)
+
+##############################################################################
+# Plot Results
+# ----------------
+#
+# Here we plot the results to compare the two pipelines
+
+fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])
+
+sns.stripplot(
+    data=results,
+    y="score",
+    x="pipeline",
+    ax=ax,
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sns.pointplot(data=results, y="score", x="pipeline", ax=ax, palette="Set1")
+
+ax.set_ylabel("ROC AUC")
+ax.set_ylim(0.5, 1)
+
+plt.show()
diff --git a/docs/_downloads/3bb6d35e266c94de44c44ec75aa9d550/plot_mne_and_scikit_estimators.py b/docs/_downloads/3bb6d35e266c94de44c44ec75aa9d550/plot_mne_and_scikit_estimators.py
new file mode 100644
index 00000000..4bfa5cf9
--- /dev/null
+++ b/docs/_downloads/3bb6d35e266c94de44c44ec75aa9d550/plot_mne_and_scikit_estimators.py
@@ -0,0 +1,199 @@
+"""
+==========================
+MNE Epochs-based pipelines
+==========================
+
+This example shows how to use machine learning pipeline based on MNE Epochs
+instead of Numpy arrays. This is useful to make the most of the MNE code base
+and to embed EEG specific code inside sklearn pipelines.
+
+We will compare different pipelines for P300:
+- Logistic regression, based on MNE Epochs
+- XDAWN and Logistic Regression (LR), based on MNE Epochs
+- XDAWN extended covariance and LR on tangent space, based on Numpy
+
+"""
+
+# Authors: Sylvain Chevallier
+#
+# License: BSD (3-clause)
+# sphinx_gallery_thumbnail_number = 2
+
+import warnings
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from mne.decoding import Vectorizer
+from mne.preprocessing import Xdawn
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+import moabb
+from moabb.analysis.meta_analysis import (  # noqa: E501
+    compute_dataset_statistics,
+    find_significant_differences,
+)
+from moabb.analysis.plotting import paired_plot, summary_plot
+from moabb.datasets import BNCI2014_009
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import P300
+
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+moabb.set_log_level("info")
+
+###############################################################################
+# Loading Dataset
+# ---------------
+#
+# Load 2 subjects of BNCI 2014-009 dataset, with 3 session each
+
+dataset = BNCI2014_009()
+dataset.subject_list = dataset.subject_list[:3]
+datasets = [dataset]
+paradigm = P300()
+
+##############################################################################
+# Get Data (optional)
+# -------------------
+#
+# To get access to the EEG signals downloaded from the dataset, you could
+# use ``dataset.get_data([subject_id)`` to obtain the EEG as MNE Epochs, stored
+# in a dictionary of sessions and runs.
+# The ``paradigm.get_data(dataset=dataset, subjects=[subject_id])`` allows to
+# obtain the preprocessed EEG data, the labels and the meta information. By
+# default, the EEG is return as a Numpy array. With ``return_epochs=True``, MNE
+# Epochs are returned.
+
+subject_list = [1]
+sessions = dataset.get_data(subject_list)
+X, labels, meta = paradigm.get_data(dataset=dataset, subjects=subject_list)
+epochs, labels, meta = paradigm.get_data(
+    dataset=dataset, subjects=subject_list, return_epochs=True
+)
+
+
+##############################################################################
+# A Simple MNE Pipeline
+# ---------------------
+#
+# Using ``return_epochs=True`` in the evaluation, it is possible to design a
+# pipeline based on MNE Epochs input. Let's create a simple one, that
+# reshape the input data from epochs, rescale the data and uses a logistic
+# regression to classify the data. We will need to write a basic Transformer
+# estimator, that complies with
+# `sklearn convention <https://scikit-learn.org/stable/developers/develop.html>`_.
+# This transformer will extract the data from an input Epoch, and reshapes into
+# 2D array.
+
+
+class MyVectorizer(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        arr = X.get_data()
+        self.features_shape_ = arr.shape[1:]
+        return self
+
+    def transform(self, X, y=None):
+        arr = X.get_data()
+        return arr.reshape(len(arr), -1)
+
+
+##############################################################################
+# We will define a pipeline that is based on this new class, using a scaler
+# and a logistic regression. This pipeline is evaluated across session using
+# ROC-AUC metric.
+
+mne_ppl = {}
+mne_ppl["MNE LR"] = make_pipeline(
+    MyVectorizer(), StandardScaler(), LogisticRegression(penalty="l1", solver="liblinear")
+)
+
+mne_eval = CrossSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    suffix="examples",
+    overwrite=True,
+    return_epochs=True,
+)
+mne_res = mne_eval.process(mne_ppl)
+
+##############################################################################
+# Advanced MNE Pipeline
+# ---------------------
+#
+# In some case, the MNE pipeline should have access to the original labels from
+# the dataset. This is the case for the XDAWN code of MNE. One could pass
+# `mne_labels` to evaluation in order to keep this label.
+# As an example, we will define a pipeline that computes an XDAWN filter, rescale,
+# then apply a logistic regression.
+
+mne_adv = {}
+mne_adv["XDAWN LR"] = make_pipeline(
+    Xdawn(n_components=5, reg="ledoit_wolf", correct_overlap=False),
+    Vectorizer(),
+    StandardScaler(),
+    LogisticRegression(penalty="l1", solver="liblinear"),
+)
+adv_eval = CrossSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    suffix="examples",
+    overwrite=True,
+    return_epochs=True,
+    mne_labels=True,
+)
+adv_res = mne_eval.process(mne_adv)
+
+###############################################################################
+# Numpy-based Pipeline
+# --------------------
+#
+# For the comparison, we will define a Numpy-based pipeline that relies on
+# pyriemann to estimate XDAWN-extended covariance matrices that are projected
+# on the tangent space and classified with a logistic regression.
+
+sk_ppl = {}
+sk_ppl["RG LR"] = make_pipeline(
+    XdawnCovariances(nfilter=5, estimator="lwf", xdawn_estimator="scm"),
+    TangentSpace(),
+    LogisticRegression(penalty="l1", solver="liblinear"),
+)
+sk_eval = CrossSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    suffix="examples",
+    overwrite=True,
+)
+sk_res = sk_eval.process(sk_ppl)
+
+###############################################################################
+# Combining Results
+# -----------------
+#
+# Even if the results have been obtained by different evaluation processes, it
+# is possible to combine the resulting DataFrames to analyze and plot the
+# results.
+
+all_res = pd.concat([mne_res, adv_res, sk_res])
+
+##############################################################################
+# We could compare the Euclidean and Riemannian performance using a `paired_plot`
+
+paired_plot(all_res, "XDAWN LR", "RG LR")
+
+##############################################################################
+# All the results could be compared and statistical analysis could highlight the
+# differences between pipelines.
+
+stats = compute_dataset_statistics(all_res)
+P, T = find_significant_differences(stats)
+summary_plot(P, T)
+plt.show()
diff --git a/docs/_downloads/3f1909c085169067146e835f0f34191d/noplot_tutorial_5_build_a_custom_dataset.py b/docs/_downloads/3f1909c085169067146e835f0f34191d/noplot_tutorial_5_build_a_custom_dataset.py
new file mode 100644
index 00000000..24c7ee9a
--- /dev/null
+++ b/docs/_downloads/3f1909c085169067146e835f0f34191d/noplot_tutorial_5_build_a_custom_dataset.py
@@ -0,0 +1,122 @@
+"""
+====================================
+Tutorial 5: Creating a dataset class
+====================================
+"""
+
+# Author: Gregoire Cattan
+#
+# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
+
+from pyriemann.classification import MDM
+from pyriemann.estimation import ERPCovariances
+from sklearn.pipeline import make_pipeline
+
+from moabb.datasets import Cattan2019_VR
+from moabb.datasets.braininvaders import BI2014a
+from moabb.datasets.compound_dataset import CompoundDataset
+from moabb.datasets.utils import blocks_reps
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms.p300 import P300
+
+
+##############################################################################
+# Initialization
+# ------------------
+#
+# This tutorial illustrates how to use the CompoundDataset to:
+# 1) Select a few subjects/sessions/runs in an existing dataset
+# 2) Merge two CompoundDataset into a new one
+# 3) ... and finally use this new dataset on a pipeline
+# (this steps is not specific to CompoundDataset)
+#
+# Let's define a paradigm and a pipeline for evaluation first.
+
+paradigm = P300()
+pipelines = {}
+pipelines["MDM"] = make_pipeline(ERPCovariances(estimator="lwf"), MDM(metric="riemann"))
+
+
+##############################################################################
+# Creation a selection of subject
+# ------------------
+#
+# We are going to great two CompoundDataset, namely CustomDataset1 &  2.
+# A CompoundDataset accepts a subjects_list of subjects.
+# It is a list of tuple. A tuple contains 4 values:
+# - the original dataset
+# - the subject number to select
+# - the sessions. It can be:
+#   - a session name ('0')
+#   - a list of sessions (['0', '1'])
+#   - `None` to select all the sessions attributed to a subject
+# - the runs. As for sessions, it can be a single run name, a list or `None`` (to select all runs).
+
+
+class CustomDataset1(CompoundDataset):
+    def __init__(self):
+        biVR = Cattan2019_VR(virtual_reality=True, screen_display=True)
+        runs = blocks_reps([0, 2], [0, 1, 2, 3, 4], biVR.n_repetitions)
+        subjects_list = [
+            (biVR, 1, "0VR", runs),
+            (biVR, 2, "0VR", runs),
+        ]
+        CompoundDataset.__init__(
+            self,
+            subjects_list=subjects_list,
+            code="CustomDataset1",
+            interval=[0, 1.0],
+        )
+
+
+class CustomDataset2(CompoundDataset):
+    def __init__(self):
+        bi2014 = BI2014a()
+        subjects_list = [
+            (bi2014, 4, None, None),
+            (bi2014, 7, None, None),
+        ]
+        CompoundDataset.__init__(
+            self,
+            subjects_list=subjects_list,
+            code="CustomDataset2",
+            interval=[0, 1.0],
+        )
+
+
+##############################################################################
+# Merging the datasets
+# ------------------
+#
+# We are now going to merge the two CompoundDataset into a single one.
+# The implementation is straight forward. Instead of providing a list of subjects,
+# you should provide a list of CompoundDataset.
+# subjects_list = [CustomDataset1(), CustomDataset2()]
+
+
+class CustomDataset3(CompoundDataset):
+    def __init__(self):
+        subjects_list = [CustomDataset1(), CustomDataset2()]
+        CompoundDataset.__init__(
+            self,
+            subjects_list=subjects_list,
+            code="CustomDataset3",
+            interval=[0, 1.0],
+        )
+
+
+##############################################################################
+# Evaluate and display
+# ------------------
+#
+# Let's use a WithinSessionEvaluation to evaluate our new dataset.
+# If you already new how to do this, nothing changed:
+# The CompoundDataset can be used as a `normal` dataset.
+
+datasets = [CustomDataset3()]
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, overwrite=False, suffix="newdataset"
+)
+scores = evaluation.process(pipelines)
+
+print(scores)
diff --git a/docs/_downloads/45332a7ce05680b6eb2282c04cab7d5b/plot_cross_session_multiple_datasets.ipynb b/docs/_downloads/45332a7ce05680b6eb2282c04cab7d5b/plot_cross_session_multiple_datasets.ipynb
new file mode 100644
index 00000000..bac40dbe
--- /dev/null
+++ b/docs/_downloads/45332a7ce05680b6eb2282c04cab7d5b/plot_cross_session_multiple_datasets.ipynb
@@ -0,0 +1,162 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cross-Session on Multiple Datasets\n\nThis example shows how to perform a cross-session analysis on two MI datasets\nusing a CSP+LDA pipeline\n\nThe cross session evaluation context will evaluate performance using a leave\none session out cross-validation. For each session in the dataset, a model\nis trained on every other session and performance are evaluated on the current\nsession.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>\n#\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001, Zhou2016\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading Dataset\n\nLoad 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 session each\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subj = [1, 2]\ndatasets = [Zhou2016(), BNCI2014_001()]\nfor d in datasets:\n    d.subject_list = subj"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Choose Paradigm\n\nWe select the paradigm MI, applying a bandpass filter (8-35 Hz) on\nthe data and we will keep only left- and right-hand motor imagery\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery(fmin=8, fmax=35)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nUse the Common Spatial Patterns with 8 components and a Linear Discriminant\nAnalysis classifier.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipeline = {}\npipeline[\"CSP+LDA\"] = make_pipeline(CSP(n_components=8), LDA())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get Data (optional)\n\nTo get access to the EEG signals downloaded from the dataset, you could\nuse `dataset.get_data(subjects=[subject_id])` to obtain the EEG under\nan MNE format, stored in a dictionary of sessions and runs.\nOtherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])`\nallows to obtain the EEG data in sklearn format, the labels and the meta\ninformation. The data are preprocessed according to the paradigm\nrequirements.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# X_all, labels_all, meta_all = [], [], []\n# for d in datasets:\n#     # sessions = d.get_data(subjects=[2])\n#     X, labels, meta = paradigm.get_data(dataset=d, subjects=[2])\n#     X_all.append(X)\n#     labels_all.append(labels)\n#     meta_all.append(meta)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nThe evaluation will return a DataFrame containing a single AUC score for\neach subject / session of the dataset, and for each pipeline.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "overwrite = True  # set to True if we want to overwrite cached results\n\nevaluation = CrossSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"examples\", overwrite=overwrite\n)\nresults = evaluation.process(pipeline)\n\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we plot the results, indicating the score for each session and subject\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sns.catplot(\n    data=results,\n    x=\"session\",\n    y=\"score\",\n    hue=\"subject\",\n    col=\"dataset\",\n    kind=\"bar\",\n    palette=\"viridis\",\n)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/47ae966f3ab36fc8884cda73c38e648d/plot_benchmark_grid_search.ipynb b/docs/_downloads/47ae966f3ab36fc8884cda73c38e648d/plot_benchmark_grid_search.ipynb
new file mode 100644
index 00000000..901ce00a
--- /dev/null
+++ b/docs/_downloads/47ae966f3ab36fc8884cda73c38e648d/plot_benchmark_grid_search.ipynb
@@ -0,0 +1,90 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmarking with MOABB with Grid Search\n\nThis example shows how to use MOABB to benchmark a set of pipelines\non all available datasets. In particular we run the Gridsearch to select the best hyperparameter of some pipelines\nand save the gridsearch.\nFor this example, we will use only one dataset to keep the computation time low, but this benchmark is designed\nto easily scale to many datasets.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Igor Carrara <igor.carrara@inria.fr>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nfrom moabb import benchmark, set_log_level\nfrom moabb.analysis.plotting import score_plot\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use only the dataset 'Zhou 2016'.\n\n## Running the benchmark\n\nThe benchmark is run using the ``benchmark`` function. You need to specify the\nfolder containing the pipelines to use, the kind of evaluation and the paradigm\nto use. By default, the benchmark will use all available datasets for all\nparadigms listed in the pipelines. You could restrict to specific evaluation and\nparadigm using the ``evaluations`` and ``paradigms`` arguments.\n\nTo save computation time, the results are cached. If you want to re-run the\nbenchmark, you can set the ``overwrite`` argument to ``True``.\n\nIt is possible to indicate the folder to cache the results and the one to save\nthe analysis & figures. By default, the results are saved in the ``results``\nfolder, and the analysis & figures are saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# In the results folder we will save the gridsearch evaluation\n# When write the pipeline in ylm file we need to specify the parameter that we want to test, in format\n# pipeline-name__estimator-name_parameter. Note that pipeline and estimator names MUST\n# be in lower case (no capital letters allowed).\n# If the grid search is already implemented it will load the previous results\n\nresults = benchmark(\n    pipelines=\"./pipelines_grid/\",\n    evaluations=[\"WithinSession\"],\n    paradigms=[\"LeftRightImagery\"],\n    include_datasets=[\"Zhou2016\"],\n    results=\"./results/\",\n    overwrite=False,\n    plot=False,\n    output=\"./benchmark/\",\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Benchmark prints a summary of the results. Detailed results are saved in a\npandas dataframe, and can be used to generate figures. The analysis & figures\nare saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "score_plot(results)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/4819137c12db5666618aa0e7e8603bce/noplot_tutorial_5_build_a_custom_dataset.ipynb b/docs/_downloads/4819137c12db5666618aa0e7e8603bce/noplot_tutorial_5_build_a_custom_dataset.ipynb
new file mode 100644
index 00000000..345830d0
--- /dev/null
+++ b/docs/_downloads/4819137c12db5666618aa0e7e8603bce/noplot_tutorial_5_build_a_custom_dataset.ipynb
@@ -0,0 +1,126 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Tutorial 5: Creating a dataset class\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Gregoire Cattan\n#\n# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019\n\nfrom pyriemann.classification import MDM\nfrom pyriemann.estimation import ERPCovariances\nfrom sklearn.pipeline import make_pipeline\n\nfrom moabb.datasets import Cattan2019_VR\nfrom moabb.datasets.braininvaders import BI2014a\nfrom moabb.datasets.compound_dataset import CompoundDataset\nfrom moabb.datasets.utils import blocks_reps\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms.p300 import P300"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n\nThis tutorial illustrates how to use the CompoundDataset to:\n1) Select a few subjects/sessions/runs in an existing dataset\n2) Merge two CompoundDataset into a new one\n3) ... and finally use this new dataset on a pipeline\n(this steps is not specific to CompoundDataset)\n\nLet's define a paradigm and a pipeline for evaluation first.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = P300()\npipelines = {}\npipelines[\"MDM\"] = make_pipeline(ERPCovariances(estimator=\"lwf\"), MDM(metric=\"riemann\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creation a selection of subject\n\nWe are going to great two CompoundDataset, namely CustomDataset1 &  2.\nA CompoundDataset accepts a subjects_list of subjects.\nIt is a list of tuple. A tuple contains 4 values:\n- the original dataset\n- the subject number to select\n- the sessions. It can be:\n  - a session name ('0')\n  - a list of sessions (['0', '1'])\n  - `None` to select all the sessions attributed to a subject\n- the runs. As for sessions, it can be a single run name, a list or `None`` (to select all runs).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "class CustomDataset1(CompoundDataset):\n    def __init__(self):\n        biVR = Cattan2019_VR(virtual_reality=True, screen_display=True)\n        runs = blocks_reps([0, 2], [0, 1, 2, 3, 4], biVR.n_repetitions)\n        subjects_list = [\n            (biVR, 1, \"0VR\", runs),\n            (biVR, 2, \"0VR\", runs),\n        ]\n        CompoundDataset.__init__(\n            self,\n            subjects_list=subjects_list,\n            code=\"CustomDataset1\",\n            interval=[0, 1.0],\n        )\n\n\nclass CustomDataset2(CompoundDataset):\n    def __init__(self):\n        bi2014 = BI2014a()\n        subjects_list = [\n            (bi2014, 4, None, None),\n            (bi2014, 7, None, None),\n        ]\n        CompoundDataset.__init__(\n            self,\n            subjects_list=subjects_list,\n            code=\"CustomDataset2\",\n            interval=[0, 1.0],\n        )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Merging the datasets\n\nWe are now going to merge the two CompoundDataset into a single one.\nThe implementation is straight forward. Instead of providing a list of subjects,\nyou should provide a list of CompoundDataset.\nsubjects_list = [CustomDataset1(), CustomDataset2()]\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "class CustomDataset3(CompoundDataset):\n    def __init__(self):\n        subjects_list = [CustomDataset1(), CustomDataset2()]\n        CompoundDataset.__init__(\n            self,\n            subjects_list=subjects_list,\n            code=\"CustomDataset3\",\n            interval=[0, 1.0],\n        )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluate and display\n\nLet's use a WithinSessionEvaluation to evaluate our new dataset.\nIf you already new how to do this, nothing changed:\nThe CompoundDataset can be used as a `normal` dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "datasets = [CustomDataset3()]\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, overwrite=False, suffix=\"newdataset\"\n)\nscores = evaluation.process(pipelines)\n\nprint(scores)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/4c45605b7f9e2012cf86c10fa0f2ed72/plot_filterbank_csp_vs_csp.py b/docs/_downloads/4c45605b7f9e2012cf86c10fa0f2ed72/plot_filterbank_csp_vs_csp.py
new file mode 100644
index 00000000..b982073d
--- /dev/null
+++ b/docs/_downloads/4c45605b7f9e2012cf86c10fa0f2ed72/plot_filterbank_csp_vs_csp.py
@@ -0,0 +1,129 @@
+"""
+=========================
+FilterBank CSP versus CSP
+=========================
+
+This example show a comparison of CSP versus FilterBank CSP on the
+very popular dataset 2a from the BCI competition IV.
+"""
+
+# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
+#
+# License: BSD (3-clause)
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from mne.decoding import CSP
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import FilterBankLeftRightImagery, LeftRightImagery
+from moabb.pipelines.utils import FilterBank
+
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# The CSP implementation from MNE is used. We selected 8 CSP components, as
+# usually done in the literature.
+#
+# The second pipeline is the filter bank CSP. We use the FilterBank object
+# with a CSP estimator. We set up the CSP to 4 components, to compensate for
+# the higher dimensionality.
+#
+# The two pipelines will be applied on two different paradigm, so they have
+# their own dict.
+
+pipelines = {}
+pipelines["CSP+LDA"] = make_pipeline(CSP(n_components=8), LDA())
+
+pipelines_fb = {}
+pipelines_fb["FBCSP+LDA"] = make_pipeline(FilterBank(CSP(n_components=4)), LDA())
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# Since two different preprocessing will be applied, we have two different
+# paradigm objects. We have to make sure their filter matches so the comparison
+# will be fair.
+#
+# The first one is a standard `LeftRightImagery` with a 8 to 35 Hz broadband
+# filter.
+#
+# The second is a `FilterBankLeftRightImagery` with a bank of 2 filters, ranging
+# from 8 to 35 Hz.
+
+# Because this is being auto-generated we only use 2 subjects
+dataset = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:2]
+datasets = [dataset]
+overwrite = False  # set to True if we want to overwrite cached results
+
+# broadband filters
+fmin = 8
+fmax = 35
+paradigm = LeftRightImagery(fmin=fmin, fmax=fmax)
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
+)
+results = evaluation.process(pipelines)
+
+# Bank of 2 filters
+filters = [[8, 24], [16, 32]]
+paradigm = FilterBankLeftRightImagery(filters=filters)
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
+)
+results_fb = evaluation.process(pipelines_fb)
+
+###############################################################################
+# After processing the two, we simply concatenate the results.
+
+results = pd.concat([results, results_fb])
+
+##############################################################################
+# Plot Results
+# ----------------
+#
+# Here we plot the results via seaborn. We first display a pointplot
+# with the average performance of each pipeline across session and subjects.
+# The second plot is a paired scatter plot. Each point representing the score
+# of a single session. An algorithm will outperform another is most of the
+# points are in its quadrant.
+
+fig, axes = plt.subplots(1, 2, figsize=[8, 4], sharey=True)
+
+sns.stripplot(
+    data=results,
+    y="score",
+    x="pipeline",
+    ax=axes[0],
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sns.pointplot(data=results, y="score", x="pipeline", ax=axes[0], palette="Set1")
+
+axes[0].set_ylabel("ROC AUC")
+axes[0].set_ylim(0.5, 1)
+
+# paired plot
+paired = results.pivot_table(
+    values="score", columns="pipeline", index=["subject", "session"]
+)
+paired = paired.reset_index()
+
+sns.regplot(data=paired, y="FBCSP+LDA", x="CSP+LDA", ax=axes[1], fit_reg=False)
+axes[1].plot([0, 1], [0, 1], ls="--", c="k")
+axes[1].set_xlim(0.5, 1)
+
+plt.show()
diff --git a/docs/_downloads/4ecaaa7a22e7ba0d94b874867e072727/tutorial_2_using_mulitple_datasets.ipynb b/docs/_downloads/4ecaaa7a22e7ba0d94b874867e072727/tutorial_2_using_mulitple_datasets.ipynb
new file mode 100644
index 00000000..7f5b32f0
--- /dev/null
+++ b/docs/_downloads/4ecaaa7a22e7ba0d94b874867e072727/tutorial_2_using_mulitple_datasets.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Tutorial 2: Using multiple datasets\n\nWe extend the previous example to a case where we want to analyze the score of\na classifier with three different MI datasets instead of just one. As before,\nwe begin by importing all relevant libraries.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier\n#\n# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport mne\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001, Zhou2016\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nmoabb.set_log_level(\"info\")\nmne.set_log_level(\"CRITICAL\")\nwarnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initializing Datasets\n\nWe instantiate the two different datasets that follow the MI paradigm\n(with left-hand/right-hand classes) but were recorded with different number\nof electrodes, different number of trials, etc.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "datasets = [Zhou2016(), BNCI2014_001()]\nsubj = [1, 2, 3]\nfor d in datasets:\n    d.subject_list = subj"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following lines go exactly as in the previous example, where we end up\nobtaining a pandas dataframe containing the results of the evaluation. We\ncould set `overwrite` to False to cache the results, avoiding to restart all\nthe evaluation from scratch if a problem occurs.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, overwrite=False\n)\npipeline = make_pipeline(CSP(n_components=8), LDA())\nresults = evaluation.process({\"csp+lda\": pipeline})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting Results\n\nWe plot the results using the seaborn library. Note how easy it\nis to plot the results from the three datasets with just one line.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results[\"subj\"] = [str(resi).zfill(2) for resi in results[\"subject\"]]\ng = sns.catplot(\n    kind=\"bar\",\n    x=\"score\",\n    y=\"subj\",\n    col=\"dataset\",\n    data=results,\n    orient=\"h\",\n    palette=\"viridis\",\n)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/51a1867bba0844057530c56f925fbc95/plot_benchmark_DL.ipynb b/docs/_downloads/51a1867bba0844057530c56f925fbc95/plot_benchmark_DL.ipynb
new file mode 100644
index 00000000..1a0ccfb8
--- /dev/null
+++ b/docs/_downloads/51a1867bba0844057530c56f925fbc95/plot_benchmark_DL.ipynb
@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmarking on MOABB with Tensorflow deep net architectures\nThis example shows how to use MOABB to benchmark a set of Deep Learning pipeline (Tensorflow)\non all available datasets.\nFor this example, we will use only one dataset to keep the computation time low, but this benchmark is designed\nto easily scale to many datasets.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Igor Carrara <igor.carrara@inria.fr>\n#\n# License: BSD (3-clause)\n\nimport os\n\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom absl.logging import ERROR, set_verbosity\nfrom tensorflow import keras\n\nfrom moabb import benchmark, set_log_level\nfrom moabb.analysis.plotting import score_plot\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.utils import setup_seed\n\n\nset_log_level(\"info\")\n# Avoid output Warning\nset_verbosity(ERROR)\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n\n# Print Information Tensorflow\nprint(f\"Tensorflow Version: {tf.__version__}\")\nprint(f\"Keras Version: {keras.__version__}\")\n\nCPU = len(tf.config.list_physical_devices(\"CPU\")) > 0\nprint(\"CPU is\", \"AVAILABLE\" if CPU else \"NOT AVAILABLE\")\n\nGPU = len(tf.config.list_physical_devices(\"GPU\")) > 0\nprint(\"GPU is\", \"AVAILABLE\" if GPU else \"NOT AVAILABLE\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use only the dataset ``BNCI2014_001``.\n\n## Running the benchmark\n\nThe benchmark is run using the ``benchmark`` function. You need to specify the\nfolder containing the pipelines to use, the kind of evaluation and the paradigm\nto use. By default, the benchmark will use all available datasets for all\nparadigms listed in the pipelines. You could restrict to specific evaluation and\nparadigm using the ``evaluations`` and ``paradigms`` arguments.\n\nTo save computation time, the results are cached. If you want to re-run the\nbenchmark, you can set the ``overwrite`` argument to ``True``.\n\nIt is possible to indicate the folder to cache the results and the one to save\nthe analysis & figures. By default, the results are saved in the ``results``\nfolder, and the analysis & figures are saved in the ``benchmark`` folder.\n\nThis code is implemented to run on CPU. If you're using a GPU, do not use multithreading\n(i.e. set n_jobs=1)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Set up reproducibility of Tensorflow\nsetup_seed(42)\n\n# Restrict this example only on the first two subject of BNCI2014_001\ndataset = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:2]\ndatasets = [dataset]\n\nresults = benchmark(\n    pipelines=\"./pipelines_DL\",\n    evaluations=[\"WithinSession\"],\n    paradigms=[\"LeftRightImagery\"],\n    include_datasets=datasets,\n    results=\"./results/\",\n    overwrite=False,\n    plot=False,\n    output=\"./benchmark/\",\n    n_jobs=-1,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The deep learning architectures implemented in MOABB are:\n- Shallow Convolutional Network [1]_\n- Deep Convolutional Network [1]_\n- EEGNet [2]_\n- EEGTCNet [3]_\n- EEGNex [4]_\n- EEGITNet [5]_\n\nBenchmark prints a summary of the results. Detailed results are saved in a\npandas dataframe, and can be used to generate figures. The analysis & figures\nare saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "score_plot(results)\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,\n   Glasstetter, M., Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).\n   [Deep learning with convolutional neural networks for EEG decoding and\n   visualization](https://doi.org/10.1002/hbm.23730).\n   Human brain mapping, 38(11), 5391-5420.\n.. [2] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,\n   Hung, C. P., & Lance, B. J. (2018). [EEGNet: a compact convolutional neural\n   network for EEG-based brain-computer interfaces.](https://doi.org/10.1088/1741-2552/aace8c)\n   Journal of neural engineering, 15(5), 056013.\n.. [3] Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., &\n   Benini, L. (2020, October). [EEG-TCNet: An accurate temporal convolutional\n   network for embedded motor-imagery brain-machine interfaces.](https://doi.org/10.1109/SMC42975.2020.9283028)\n   In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)\n   (pp. 2958-2965). IEEE.\n.. [4] Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2022). [Toward reliable\n   signals decoding for electroencephalogram: A benchmark study to EEGNeX.](https://doi.org/10.48550/arXiv.2207.12369)\n   arXiv preprint arXiv:2207.12369.\n.. [5] Salami, A., Andreu-Perez, J., & Gillmeister, H. (2022). [EEG-ITNet: An\n   explainable inception temporal convolutional network for motor imagery\n   classification](https://doi.org/10.1109/ACCESS.2022.3161489).\n   IEEE Access, 10, 36672-36685.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/5303bff802560c798e898bab8eb895a3/noplot_vr_pc_p300_different_epoch_size.py b/docs/_downloads/5303bff802560c798e898bab8eb895a3/noplot_vr_pc_p300_different_epoch_size.py
new file mode 100644
index 00000000..222a668e
--- /dev/null
+++ b/docs/_downloads/5303bff802560c798e898bab8eb895a3/noplot_vr_pc_p300_different_epoch_size.py
@@ -0,0 +1,149 @@
+""" Example of P300 classification with different epoch size.
+
+=======================================
+Changing epoch size in P300 VR dataset
+=======================================
+
+This example shows how to extract the epochs from the P300-VR dataset of a given
+subject and then classify them using Riemannian Geometry framework for BCI.
+We compare the scores in the VR and PC conditions, using different epoch size.
+
+This example demonstrates the use of `get_block_repetition`, which allows
+to specify the experimental blocks and repetitions for analysis.
+"""
+
+# Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com>
+# Modified by: Gregoire Cattan <gcattan@hotmail.fr>
+# License: BSD (3-clause)
+
+import warnings
+
+import numpy as np
+import pandas as pd
+from pyriemann.classification import MDM
+from pyriemann.estimation import ERPCovariances
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import KFold
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder
+from tqdm import tqdm
+
+from moabb.datasets import Cattan2019_VR
+from moabb.paradigms import P300
+
+
+warnings.filterwarnings("ignore")
+
+###############################################################################
+# Initialization
+# ---------------
+#
+# 1) Create an instance of the dataset.
+# 2) Create an instance of a P300 paradigm.
+#    By default filtering between 1-24 Hz
+#    with epochs of length 1s.
+#    In this example we will be modifying the length of the epochs, by
+#    changing the `tmax` attribute of the paradigm.
+# 3) Encode categorical variable (Target/NonTarget) to numerical values.
+#    We will be using label encoding.
+
+dataset = Cattan2019_VR()
+paradigm = P300()
+le = LabelEncoder().fit(["Target", "NonTarget"])
+
+# change this to include more subjects
+nsubjects = 2
+
+###############################################################################
+# Validation
+# ---------------
+#
+# We will perform a 3-folds validation for each combination of
+# tmax, subjects and experimental conditions (VR or PC).
+#
+# Not all the data will be used for this validation.
+# The Cattan2019_VR dataset contains the data from a randomized experiment.
+# We will only be using the two first repetitions of the 12 experimental blocks.
+# Data will be selected thanks to the `get_block_repetition` method.
+
+# Contains the score for all combination of tmax, subjects
+# and experimental condition (VR or PC).
+scores = []
+
+# Init 3-folds validation.
+kf = KFold(n_splits=3)
+
+# Select the first two repetitions.
+repetitions = [1, 2]
+
+# Generate all possible arrangement with the 12 blocks.
+blocks = np.arange(1, 12 + 1)
+
+# run validation for each combination.
+for tmax in [0.2, 1.0]:
+    paradigm.tmax = tmax
+
+    for subject in tqdm(dataset.subject_list[:nsubjects]):
+        # Note: here we are adding `tmax` to scores_subject,
+        # although `tmax` is defined outside the scope of this inner loop.
+        # The reason behind is to facilitate the conversion from array to dataframe at the end.
+        scores_subject = [tmax, subject]
+
+        for condition in ["VR", "PC"]:
+            print(f"subject {subject}, {condition}, tmax {tmax}")
+
+            # Rather than creating a new instance depending on the condition,
+            # let's change the attribute value to download the correct data.
+            dataset.virtual_reality = condition == "VR"
+            dataset.personal_computer = condition == "PC"
+
+            auc = []
+
+            # Split in training and testing blocks, and fit/predict.
+            # This loop will run 3 times as we are using a 3-folds validation
+            for train_idx, test_idx in kf.split(np.arange(12)):
+                # Note the use of the `get_block_repetition` method,
+                # to select the appropriate number of blocks and repetitions:
+                # - 8 blocks for training, 4 for testing
+                # - only the first two repetitions inside each blocks
+                X_train, y_train, _ = dataset.get_block_repetition(
+                    paradigm, [subject], blocks[train_idx], repetitions
+                )
+
+                X_test, y_test, _ = dataset.get_block_repetition(
+                    paradigm, [subject], blocks[test_idx], repetitions
+                )
+
+                # We use riemannian geometry processing techniques with MDM algorithm.
+                pipe = make_pipeline(ERPCovariances(estimator="lwf"), MDM())
+                pipe.fit(X_train, y_train)
+                y_pred = pipe.predict(X_test)
+
+                # y_test and y_pred contains categorical variable (Target/NonTarget).
+                # To use a metric, we need to convert target information to numerical values.
+                y_test = le.transform(y_test)
+                y_pred = le.transform(y_pred)
+
+                # We use the roc_auc_score, which is a reliable metric for multi-class problem.
+                auc.append(roc_auc_score(y_test, y_pred))
+
+            # stock scores
+            scores_subject.append(np.mean(auc))
+
+        scores.append(scores_subject)
+
+###############################################################################
+# Display of the data
+# ---------------
+#
+# Let's transform or array to a dataframe.
+# We can then print it on the console, and
+# plot the mean AUC as a function of the epoch length.
+
+df = pd.DataFrame(scores, columns=["tmax", "subject", "VR", "PC"])
+
+print(df)
+
+df.groupby("tmax").mean().plot(
+    y=["VR", "PC"], title="Mean AUC as a function of the epoch length"
+)
diff --git a/docs/_downloads/5459b5c2be76a529eb282ae496a50415/plot_cross_subject_ssvep.py b/docs/_downloads/5459b5c2be76a529eb282ae496a50415/plot_cross_subject_ssvep.py
new file mode 100644
index 00000000..5feffeb2
--- /dev/null
+++ b/docs/_downloads/5459b5c2be76a529eb282ae496a50415/plot_cross_subject_ssvep.py
@@ -0,0 +1,180 @@
+"""
+===========================
+Cross-Subject SSVEP
+===========================
+This example shows how to perform a cross-subject analysis on an SSVEP dataset.
+We will compare four pipelines :
+
+- Riemannian Geometry
+- CCA
+- TRCA
+- MsetCCA
+
+We will use the SSVEP paradigm, which uses the AUC as metric.
+"""
+
+# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
+#
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import Kalunga2016
+from moabb.evaluations import CrossSubjectEvaluation
+from moabb.paradigms import SSVEP, FilterBankSSVEP
+from moabb.pipelines import SSVEP_CCA, SSVEP_TRCA, ExtendedSSVEPSignal, SSVEP_MsetCCA
+
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+moabb.set_log_level("info")
+
+###############################################################################
+# Loading Dataset
+# ---------------
+#
+# We will load the data from the first 2 subjects of the ``SSVEP_Exo`` dataset
+# and compare two algorithms on this set. One of the algorithms could only
+# process class associated with a stimulation frequency, we will thus drop
+# the resting class. As the resting class is the last defined class, picking
+# the first three classes (out of four) allows to focus only on the stimulation
+# frequency.
+
+n_subject = 2
+dataset = Kalunga2016()
+dataset.subject_list = dataset.subject_list[:n_subject]
+interval = dataset.interval
+
+###############################################################################
+# Choose Paradigm
+# ---------------
+#
+# We define the paradigms (SSVEP, SSVEP TRCA, SSVEP MsetCCA, and FilterBankSSVEP) and
+# use the dataset Kalunga2016. All 3 SSVEP paradigms applied a bandpass filter (10-42 Hz) on
+# the data, which include all stimuli frequencies and their first harmonics,
+# while the FilterBankSSVEP paradigm uses as many bandpass filters as
+# there are stimulation frequencies (here 3). For each stimulation frequency
+# the EEG is filtered with a 1 Hz-wide bandpass filter centered on the
+# frequency. This results in ``n_classes`` copies of the signal, filtered for each
+# class, as used in the filterbank motor imagery paradigms.
+
+paradigm = SSVEP(fmin=10, fmax=42, n_classes=3)
+paradigm_TRCA = SSVEP(fmin=10, fmax=42, n_classes=3)
+paradigm_MSET_CCA = SSVEP(fmin=10, fmax=42, n_classes=3)
+paradigm_fb = FilterBankSSVEP(filters=None, n_classes=3)
+
+###############################################################################
+# Classes are defined by the frequency of the stimulation, here we use
+# the first two frequencies of the dataset, 13 and 17 Hz.
+# The evaluation function uses a LabelEncoder, transforming them
+# to 0 and 1
+
+freqs = paradigm.used_events(dataset)
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+# The first pipeline uses Riemannian geometry, by building an extended
+# covariance matrices from the signal filtered around the considered
+# frequency and applying a logistic regression in the tangent plane.
+# The second pipeline relies on the above defined CCA classifier.
+# The third pipeline relies on the TRCA algorithm,
+# and the fourth uses the MsetCCA algorithm. Both CCA based methods
+# (i.e. CCA and MsetCCA) used 3 CCA components.
+
+pipelines_fb = {}
+pipelines_fb["RG+LogReg"] = make_pipeline(
+    ExtendedSSVEPSignal(),
+    Covariances(estimator="lwf"),
+    TangentSpace(),
+    LogisticRegression(solver="lbfgs", multi_class="auto"),
+)
+
+pipelines = {}
+pipelines["CCA"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=2))
+
+pipelines_TRCA = {}
+pipelines_TRCA["TRCA"] = make_pipeline(SSVEP_TRCA(interval=interval, freqs=freqs))
+
+pipelines_MSET_CCA = {}
+pipelines_MSET_CCA["MSET_CCA"] = make_pipeline(SSVEP_MsetCCA(freqs=freqs))
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# The evaluation will return a DataFrame containing an accuracy score for
+# each subject / session of the dataset, and for each pipeline.
+#
+# Results are saved into the database, so that if you add a new pipeline, it
+# will not run again the evaluation unless a parameter has changed. Results can
+# be overwritten if necessary.
+
+overwrite = False  # set to True if we want to overwrite cached results
+
+evaluation = CrossSubjectEvaluation(
+    paradigm=paradigm, datasets=dataset, overwrite=overwrite
+)
+results = evaluation.process(pipelines)
+
+###############################################################################
+# Filter bank processing, determine the filter automatically from the
+# stimulation frequency values of events.
+
+evaluation_fb = CrossSubjectEvaluation(
+    paradigm=paradigm_fb, datasets=dataset, overwrite=overwrite
+)
+results_fb = evaluation_fb.process(pipelines_fb)
+
+###############################################################################
+# TRCA processing also relies on filter bank that is automatically designed.
+
+evaluation_TRCA = CrossSubjectEvaluation(
+    paradigm=paradigm_TRCA, datasets=dataset, overwrite=overwrite
+)
+results_TRCA = evaluation_TRCA.process(pipelines_TRCA)
+
+###############################################################################
+# MsetCCA processing
+evaluation_MSET_CCA = CrossSubjectEvaluation(
+    paradigm=paradigm_MSET_CCA, datasets=dataset, overwrite=overwrite
+)
+results_MSET_CCA = evaluation_MSET_CCA.process(pipelines_MSET_CCA)
+
+###############################################################################
+# After processing the four, we simply concatenate the results.
+
+results = pd.concat([results, results_fb, results_TRCA, results_MSET_CCA])
+
+##############################################################################
+# Plot Results
+# ----------------
+#
+# Here we display the results as stripplot, with a pointplot for error bar.
+
+fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])
+sns.stripplot(
+    data=results,
+    y="score",
+    x="pipeline",
+    ax=ax,
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sns.pointplot(data=results, y="score", x="pipeline", ax=ax, palette="Set1")
+ax.set_ylabel("Accuracy")
+ax.set_ylim(0.1, 0.6)
+plt.show()
diff --git a/docs/_downloads/55cdcc1a57595324be0a4f77a5294187/plot_Getting_Started.py b/docs/_downloads/55cdcc1a57595324be0a4f77a5294187/plot_Getting_Started.py
new file mode 100644
index 00000000..c455c036
--- /dev/null
+++ b/docs/_downloads/55cdcc1a57595324be0a4f77a5294187/plot_Getting_Started.py
@@ -0,0 +1,119 @@
+"""
+============================
+Tutorial 0: Getting Started
+============================
+
+This tutorial takes you through a basic working example of how to use this
+codebase, including all the different components, up to the results
+generation. If you'd like to know about the statistics and plotting, see the
+next tutorial.
+
+"""
+
+# Authors: Vinay Jayaram <vinayjayaram13@gmail.com>
+#
+# License: BSD (3-clause)
+
+
+##########################################################################
+# Introduction
+# --------------------
+# To use the codebase you need an evaluation and a paradigm, some algorithms,
+# and a list of datasets to run it all on. You can find those in the following
+# submodules; detailed tutorials are given for each of them.
+
+import numpy as np
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import SVC
+
+##########################################################################
+# If you would like to specify the logging level when it is running, you can
+# use the standard python logging commands through the top-level moabb module
+import moabb
+from moabb.datasets import BNCI2014_001, utils
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+from moabb.pipelines.features import LogVariance
+
+
+##########################################################################
+# In order to create pipelines within a script, you will likely need at least
+# the make_pipeline function. They can also be specified via a .yml file. Here
+# we will make a couple pipelines just for convenience
+
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create pipelines
+# ----------------
+#
+# We create two pipelines: channel-wise log variance followed by LDA, and
+# channel-wise log variance followed by a cross-validated SVM (note that a
+# cross-validation via scikit-learn cannot be described in a .yml file). For
+# later in the process, the pipelines need to be in a dictionary where the key
+# is the name of the pipeline and the value is the Pipeline object
+
+pipelines = {}
+pipelines["AM+LDA"] = make_pipeline(LogVariance(), LDA())
+parameters = {"C": np.logspace(-2, 2, 10)}
+clf = GridSearchCV(SVC(kernel="linear"), parameters)
+pipe = make_pipeline(LogVariance(), clf)
+
+pipelines["AM+SVM"] = pipe
+
+##############################################################################
+# Datasets
+# -----------------
+#
+# Datasets can be specified in many ways: Each paradigm has a property
+# 'datasets' which returns the datasets that are appropriate for that paradigm
+
+print(LeftRightImagery().datasets)
+
+##########################################################################
+# Or you can run a search through the available datasets:
+print(utils.dataset_search(paradigm="imagery", min_subjects=6))
+
+##########################################################################
+# Or you can simply make your own list (which we do here due to computational
+# constraints)
+
+dataset = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:2]
+datasets = [dataset]
+
+##########################################################################
+# Paradigm
+# --------------------
+#
+# Paradigms define the events, epoch time, bandpass, and other preprocessing
+# parameters. They have defaults that you can read in the documentation, or you
+# can simply set them as we do here. A single paradigm defines a method for
+# going from continuous data to trial data of a fixed size. To learn more look
+# at the tutorial Exploring Paradigms
+
+fmin = 8
+fmax = 35
+paradigm = LeftRightImagery(fmin=fmin, fmax=fmax)
+
+##########################################################################
+# Evaluation
+# --------------------
+#
+# An evaluation defines how the training and test sets are chosen. This could
+# be cross-validated within a single recording, or across days, or sessions, or
+# subjects. This also is the correct place to specify multiple threads.
+
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=False
+)
+results = evaluation.process(pipelines)
+
+##########################################################################
+# Results are returned as a pandas DataFrame, and from here you can do as you
+# want with them
+
+print(results.head())
diff --git a/docs/_downloads/5657e54b8c692376a881117a99de998e/plot_benchmark.py b/docs/_downloads/5657e54b8c692376a881117a99de998e/plot_benchmark.py
new file mode 100644
index 00000000..4d39459c
--- /dev/null
+++ b/docs/_downloads/5657e54b8c692376a881117a99de998e/plot_benchmark.py
@@ -0,0 +1,102 @@
+"""Examples of how to use MOABB to benchmark pipelines.
+=======================
+Benchmarking with MOABB
+=======================
+
+This example shows how to use MOABB to benchmark a set of pipelines
+on all available datasets. For this example, we will use only one
+dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.
+"""
+
+# Authors: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>
+#
+# License: BSD (3-clause)
+
+import matplotlib.pyplot as plt
+
+from moabb import benchmark, set_log_level
+from moabb.analysis.plotting import score_plot
+from moabb.paradigms import LeftRightImagery
+
+
+set_log_level("info")
+
+###############################################################################
+# Loading the pipelines
+# ---------------------
+#
+# The ML pipelines used in benchmark are defined in YAML files, following a
+# simple format. It simplifies sharing and reusing pipelines across benchmarks,
+# reproducing state-of-the-art results.
+#
+# MOABB comes with complete list of pipelines that cover most of the successful
+# approaches in the literature. You can find them in the
+# `pipelines folder <https://github.com/NeuroTechX/moabb/tree/develop/pipelines>`_.
+# For this example, we will use a folder with only 2 pipelines, to keep the
+# computation time low.
+#
+# This is an example of a pipeline defined in YAML, defining on which paradigms it
+# can be used, the original publication, and the steps to perform using a
+# scikit-learn API. In this case, a CSP + SVM pipeline, the covariance are estimated
+# to compute a CSP filter and then a linear SVM is trained on the CSP filtered
+# signals.
+
+with open("sample_pipelines/CSP_SVM.yml", "r") as f:
+    lines = f.readlines()
+    for line in lines:
+        print(line, end="")
+
+###############################################################################
+# The ``sample_pipelines`` folder contains a second pipeline, a logistic regression
+# performed in the tangent space using Riemannian geometry.
+#
+# Selecting the datasets (optional)
+# ---------------------------------
+#
+# If you want to limit your benchmark on a subset of datasets, you can use the
+# ``include_datasets`` and ``exclude_datasets`` arguments. You will need either
+# to provide the dataset's object, or a the dataset's code. To get the list of
+# available dataset's code for a given paradigm, you can use the following command:
+
+paradigm = LeftRightImagery()
+for d in paradigm.datasets:
+    print(d.code)
+
+###############################################################################
+# In this example, we will use only the last dataset, 'Zhou 2016'.
+#
+# Running the benchmark
+# ---------------------
+#
+# The benchmark is run using the ``benchmark`` function. You need to specify the
+# folder containing the pipelines to use, the kind of evaluation and the paradigm
+# to use. By default, the benchmark will use all available datasets for all
+# paradigms listed in the pipelines. You could restrict to specific evaluation and
+# paradigm using the ``evaluations`` and ``paradigms`` arguments.
+#
+# To save computation time, the results are cached. If you want to re-run the
+# benchmark, you can set the ``overwrite`` argument to ``True``.
+#
+# It is possible to indicate the folder to cache the results and the one to save
+# the analysis & figures. By default, the results are saved in the ``results``
+# folder, and the analysis & figures are saved in the ``benchmark`` folder.
+
+results = benchmark(
+    pipelines="./sample_pipelines/",
+    evaluations=["WithinSession"],
+    paradigms=["LeftRightImagery"],
+    include_datasets=["Zhou2016"],
+    results="./results/",
+    overwrite=False,
+    plot=False,
+    output="./benchmark/",
+)
+
+###############################################################################
+# Benchmark prints a summary of the results. Detailed results are saved in a
+# pandas dataframe, and can be used to generate figures. The analysis & figures
+# are saved in the ``benchmark`` folder.
+
+score_plot(results)
+plt.show()
diff --git a/docs/_downloads/5b1f506cd53838de0dd70e569a36f198/plot_learning_curve_motor_imagery.ipynb b/docs/_downloads/5b1f506cd53838de0dd70e569a36f198/plot_learning_curve_motor_imagery.ipynb
new file mode 100644
index 00000000..c27dbaa0
--- /dev/null
+++ b/docs/_downloads/5b1f506cd53838de0dd70e569a36f198/plot_learning_curve_motor_imagery.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Within Session Motor Imagery with Learning Curve\n\nThis example shows how to perform a within session motor imagery analysis on the\nvery popular dataset 2a from the BCI competition IV.\n\nWe will compare two pipelines :\n\n- CSP + LDA\n- Riemannian Geometry + Logistic Regression\n\nWe will use the LeftRightImagery paradigm. This will restrict the analysis\nto two classes (left- vs right-hand) and use AUC as metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Original author: Alexandre Barachant <alexandre.barachant@gmail.com>\n# Learning curve modification: Jan Sosulski\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\n\nThe CSP implementation from MNE is used. We selected 8 CSP components, as\nusually done in the literature.\n\nThe Riemannian geometry pipeline consists in covariance estimation, tangent\nspace mapping and finally a logistic regression for the classification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\n\npipelines[\"CSP+LDA\"] = make_pipeline(\n    CSP(n_components=8), LDA(solver=\"lsqr\", shrinkage=\"auto\")\n)\n\npipelines[\"RG+LR\"] = make_pipeline(\n    Covariances(), TangentSpace(), LogisticRegression(solver=\"lbfgs\")\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nWe define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).\nThe evaluation will return a DataFrame containing a single AUC score for\neach subject / session of the dataset, and for each pipeline.\n\nResults are saved into the database, so that if you add a new pipeline, it\nwill not run again the evaluation unless a parameter has changed. Results can\nbe overwritten if necessary.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\ndataset = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:1]\ndatasets = [dataset]\noverwrite = True  # set to True if we want to overwrite cached results\n# Evaluate for a specific number of training samples per class\ndata_size = dict(policy=\"per_class\", value=np.array([5, 10, 30, 50]))\n# When the training data is sparse, perform more permutations than when we have a lot of data\nn_perms = np.floor(np.geomspace(20, 2, len(data_size[\"value\"]))).astype(int)\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm,\n    datasets=datasets,\n    suffix=\"examples\",\n    overwrite=overwrite,\n    data_size=data_size,\n    n_perms=n_perms,\n)\n\nresults = evaluation.process(pipelines)\n\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nWe plot the accuracy as a function of the number of training samples, for\neach pipeline\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(facecolor=\"white\", figsize=[8, 4])\n\nn_subs = len(dataset.subject_list)\n\nif n_subs > 1:\n    r = results.groupby([\"pipeline\", \"subject\", \"data_size\"]).mean().reset_index()\nelse:\n    r = results\n\nsns.pointplot(data=r, x=\"data_size\", y=\"score\", hue=\"pipeline\", ax=ax, palette=\"Set1\")\n\nerrbar_meaning = \"subjects\" if n_subs > 1 else \"permutations\"\ntitle_str = f\"Errorbar shows Mean-CI across {errbar_meaning}\"\nax.set_xlabel(\"Amount of training samples\")\nax.set_ylabel(\"ROC AUC\")\nax.set_title(title_str)\nfig.tight_layout()\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/5c05a4e8c08dd8d574a1e608b6e51c83/tutorial_3_benchmarking_multiple_pipelines.ipynb b/docs/_downloads/5c05a4e8c08dd8d574a1e608b6e51c83/tutorial_3_benchmarking_multiple_pipelines.ipynb
new file mode 100644
index 00000000..71c3d911
--- /dev/null
+++ b/docs/_downloads/5c05a4e8c08dd8d574a1e608b6e51c83/tutorial_3_benchmarking_multiple_pipelines.ipynb
@@ -0,0 +1,115 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Tutorial 3: Benchmarking multiple pipelines\n\nIn this last part, we extend the previous example by assessing the\nclassification score of not one but three classification pipelines.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier\n#\n# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport mne\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom pyriemann.classification import MDM\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import SVC\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001, Zhou2016\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nmne.set_log_level(\"CRITICAL\")\nmoabb.set_log_level(\"info\")\nwarnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating Pipelines\n\nWe instantiate the three different classiciation pipelines to be considered\nin the analysis. The object that gathers each pipeline is a dictionary. The\nfirst pipeline is the CSP+LDA that we have seen in the previous parts. The\nother two pipelines rely on Riemannian geometry, using an SVM classification\nin the tangent space of the covariance matrices estimated from the EEG or a\nMDM classifier that works directly on covariance matrices.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\npipelines[\"csp+lda\"] = make_pipeline(CSP(n_components=8), LDA())\npipelines[\"tgsp+svm\"] = make_pipeline(\n    Covariances(\"oas\"), TangentSpace(metric=\"riemann\"), SVC(kernel=\"linear\")\n)\npipelines[\"MDM\"] = make_pipeline(Covariances(\"oas\"), MDM(metric=\"riemann\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following lines go exactly as in the previous tutorial, where we end up\nobtaining a pandas dataframe containing the results of the evaluation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "datasets = [BNCI2014_001(), Zhou2016()]\nsubj = [1, 2, 3]\nfor d in datasets:\n    d.subject_list = subj\nparadigm = LeftRightImagery()\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, overwrite=False\n)\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As `overwrite` is set to False, the results from the previous tutorial are reused and\nonly the new pipelines are evaluated. The results from \"csp+lda\" are not recomputed.\nThe results are saved in ~/mne_data/results if the parameter `hdf5_path` is not set.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting Results\n\nThe following plot shows a comparison of the three classification pipelines\nfor each subject of each dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results[\"subj\"] = [str(resi).zfill(2) for resi in results[\"subject\"]]\ng = sns.catplot(\n    kind=\"bar\",\n    x=\"score\",\n    y=\"subj\",\n    hue=\"pipeline\",\n    col=\"dataset\",\n    height=12,\n    aspect=0.5,\n    data=results,\n    orient=\"h\",\n    palette=\"viridis\",\n)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/657e87726c4d308a5b176dbb4277773e/plot_select_electrodes_resample.ipynb b/docs/_downloads/657e87726c4d308a5b176dbb4277773e/plot_select_electrodes_resample.ipynb
new file mode 100644
index 00000000..8c32dd2a
--- /dev/null
+++ b/docs/_downloads/657e87726c4d308a5b176dbb4277773e/plot_select_electrodes_resample.ipynb
@@ -0,0 +1,144 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Select Electrodes and Resampling\n\nWithin paradigm, it is possible to restrict analysis only to a subset of\nelectrodes and to resample to a specific sampling rate. There is also a\nutility function to select common electrodes shared between datasets.\nThis tutorial demonstrates how to use this functionality.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>\n#\n# License: BSD (3-clause)\nimport matplotlib.pyplot as plt\nfrom mne.decoding import CSP\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.linear_model import LogisticRegression as LR\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb.analysis.plotting as moabb_plt\nfrom moabb.datasets import BNCI2014_001, Zhou2016\nfrom moabb.datasets.utils import find_intersecting_channels\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Datasets\n\nLoad 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 sessions each\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subj = [1, 2]\ndatasets = [Zhou2016(), BNCI2014_001()]\nfor d in datasets:\n    d.subject_list = subj"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Paradigm\n\nRestrict further analysis to specified channels, here C3, C4, and Cz.\nAlso, use a specific resampling. In this example, all datasets are\nset to 200 Hz.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery(channels=[\"C3\", \"C4\", \"Cz\"], resample=200.0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nThe evaluation is conducted on with CSP+LDA, only on the 3 electrodes, with\na sampling rate of 200 Hz.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "evaluation = WithinSessionEvaluation(paradigm=paradigm, datasets=datasets)\ncsp_lda = make_pipeline(CSP(n_components=2), LDA())\nts_lr = make_pipeline(\n    Covariances(estimator=\"oas\"), TangentSpace(metric=\"riemann\"), LR(C=1.0)\n)\nresults = evaluation.process({\"csp+lda\": csp_lda, \"ts+lr\": ts_lr})\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Electrode Selection\n\nIt is possible to select the electrodes that are shared by all datasets\nusing the `find_intersecting_channels` function. Datasets that have 0\noverlap with others are discarded. It returns the set of common channels,\nas well as the list of datasets with valid channels.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "electrodes, datasets = find_intersecting_channels(datasets)\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, overwrite=True, suffix=\"resample\"\n)\nresults = evaluation.process({\"csp+lda\": csp_lda, \"ts+lr\": ts_lr})\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nCompare the obtained results with the two pipelines, CSP+LDA and logistic\nregression computed in the tangent space of the covariance matrices.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = moabb_plt.paired_plot(results, \"csp+lda\", \"ts+lr\")\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
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HcmV?d00001

diff --git a/docs/_downloads/73deabcc8228302a2b46b9595661aedc/noplot_vr_pc_p300_different_epoch_size.ipynb b/docs/_downloads/73deabcc8228302a2b46b9595661aedc/noplot_vr_pc_p300_different_epoch_size.ipynb
new file mode 100644
index 00000000..2332cb34
--- /dev/null
+++ b/docs/_downloads/73deabcc8228302a2b46b9595661aedc/noplot_vr_pc_p300_different_epoch_size.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Example of P300 classification with different epoch size.\n\n# Changing epoch size in P300 VR dataset\n\nThis example shows how to extract the epochs from the P300-VR dataset of a given\nsubject and then classify them using Riemannian Geometry framework for BCI.\nWe compare the scores in the VR and PC conditions, using different epoch size.\n\nThis example demonstrates the use of `get_block_repetition`, which allows\nto specify the experimental blocks and repetitions for analysis.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com>\n# Modified by: Gregoire Cattan <gcattan@hotmail.fr>\n# License: BSD (3-clause)\n\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nfrom pyriemann.classification import MDM\nfrom pyriemann.estimation import ERPCovariances\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import LabelEncoder\nfrom tqdm import tqdm\n\nfrom moabb.datasets import Cattan2019_VR\nfrom moabb.paradigms import P300\n\n\nwarnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n\n1) Create an instance of the dataset.\n2) Create an instance of a P300 paradigm.\n   By default filtering between 1-24 Hz\n   with epochs of length 1s.\n   In this example we will be modifying the length of the epochs, by\n   changing the `tmax` attribute of the paradigm.\n3) Encode categorical variable (Target/NonTarget) to numerical values.\n   We will be using label encoding.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Cattan2019_VR()\nparadigm = P300()\nle = LabelEncoder().fit([\"Target\", \"NonTarget\"])\n\n# change this to include more subjects\nnsubjects = 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Validation\n\nWe will perform a 3-folds validation for each combination of\ntmax, subjects and experimental conditions (VR or PC).\n\nNot all the data will be used for this validation.\nThe Cattan2019_VR dataset contains the data from a randomized experiment.\nWe will only be using the two first repetitions of the 12 experimental blocks.\nData will be selected thanks to the `get_block_repetition` method.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Contains the score for all combination of tmax, subjects\n# and experimental condition (VR or PC).\nscores = []\n\n# Init 3-folds validation.\nkf = KFold(n_splits=3)\n\n# Select the first two repetitions.\nrepetitions = [1, 2]\n\n# Generate all possible arrangement with the 12 blocks.\nblocks = np.arange(1, 12 + 1)\n\n# run validation for each combination.\nfor tmax in [0.2, 1.0]:\n    paradigm.tmax = tmax\n\n    for subject in tqdm(dataset.subject_list[:nsubjects]):\n        # Note: here we are adding `tmax` to scores_subject,\n        # although `tmax` is defined outside the scope of this inner loop.\n        # The reason behind is to facilitate the conversion from array to dataframe at the end.\n        scores_subject = [tmax, subject]\n\n        for condition in [\"VR\", \"PC\"]:\n            print(f\"subject {subject}, {condition}, tmax {tmax}\")\n\n            # Rather than creating a new instance depending on the condition,\n            # let's change the attribute value to download the correct data.\n            dataset.virtual_reality = condition == \"VR\"\n            dataset.personal_computer = condition == \"PC\"\n\n            auc = []\n\n            # Split in training and testing blocks, and fit/predict.\n            # This loop will run 3 times as we are using a 3-folds validation\n            for train_idx, test_idx in kf.split(np.arange(12)):\n                # Note the use of the `get_block_repetition` method,\n                # to select the appropriate number of blocks and repetitions:\n                # - 8 blocks for training, 4 for testing\n                # - only the first two repetitions inside each blocks\n                X_train, y_train, _ = dataset.get_block_repetition(\n                    paradigm, [subject], blocks[train_idx], repetitions\n                )\n\n                X_test, y_test, _ = dataset.get_block_repetition(\n                    paradigm, [subject], blocks[test_idx], repetitions\n                )\n\n                # We use riemannian geometry processing techniques with MDM algorithm.\n                pipe = make_pipeline(ERPCovariances(estimator=\"lwf\"), MDM())\n                pipe.fit(X_train, y_train)\n                y_pred = pipe.predict(X_test)\n\n                # y_test and y_pred contains categorical variable (Target/NonTarget).\n                # To use a metric, we need to convert target information to numerical values.\n                y_test = le.transform(y_test)\n                y_pred = le.transform(y_pred)\n\n                # We use the roc_auc_score, which is a reliable metric for multi-class problem.\n                auc.append(roc_auc_score(y_test, y_pred))\n\n            # stock scores\n            scores_subject.append(np.mean(auc))\n\n        scores.append(scores_subject)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Display of the data\n\nLet's transform or array to a dataframe.\nWe can then print it on the console, and\nplot the mean AUC as a function of the epoch length.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.DataFrame(scores, columns=[\"tmax\", \"subject\", \"VR\", \"PC\"])\n\nprint(df)\n\ndf.groupby(\"tmax\").mean().plot(\n    y=[\"VR\", \"PC\"], title=\"Mean AUC as a function of the epoch length\"\n)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb b/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb
new file mode 100644
index 00000000..a93771f6
--- /dev/null
+++ b/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb
@@ -0,0 +1,126 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Load Model (Scikit, Pytorch, Keras) with MOABB\n\nThis example shows how to use load the pretrained pipeline in MOABB.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Igor Carrara <igor.carrara@inria.fr>\n#\n# License: BSD (3-clause)\n\nfrom pickle import load\n\nimport keras\nimport torch\nfrom braindecode import EEGClassifier\nfrom braindecode.models import EEGInception\nfrom scikeras.wrappers import KerasClassifier\nfrom sklearn.pipeline import Pipeline, make_pipeline\nfrom skorch.callbacks import EarlyStopping, EpochScoring\nfrom skorch.dataset import ValidSplit\n\nfrom moabb import set_log_level\nfrom moabb.pipelines.features import StandardScaler_Epoch\nfrom moabb.utils import setup_seed\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use the results computed by the following examples\n\n- plot_benchmark_\n- plot_benchmark_braindecode_\n- plot_benchmark_DL_\n---------------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Set up reproducibility of Tensorflow and PyTorch\nsetup_seed(42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Loading the Scikit-learn pipelines\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "with open(\n    \"./results/Models_WithinSession/Zhou2016/1/0/CSP + SVM/fitted_model_best.pkl\",\n    \"rb\",\n) as pickle_file:\n    CSP_SVM_Trained = load(pickle_file)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Loading the Keras model\nWe load the single Keras model, if we want we can set in the exact same pipeline.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model_Keras = keras.models.load_model(\n    \"./results/Models_WithinSession/BNCI2014-001/1/1E/Keras_DeepConvNet/kerasdeepconvnet_fitted_model_best.h5\"\n)\n# Now we need to instantiate a new SciKeras object since we only saved the Keras model\nKeras_DeepConvNet_Trained = KerasClassifier(model_Keras)\n# Create the pipelines\n\n\npipes_keras = Pipeline(\n    [\n        (\"StandardScaler_Epoch\", StandardScaler_Epoch),\n        (\"Keras_DeepConvNet_Trained\", Keras_DeepConvNet_Trained),\n    ]\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Loading the PyTorch model\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Hyperparameter\nLEARNING_RATE = 0.0001\nWEIGHT_DECAY = 0\nBATCH_SIZE = 64\nSEED = 42\nVERBOSE = 1\nEPOCH = 2\nPATIENCE = 3\n\n# Define a Skorch classifier\nclf = EEGClassifier(\n    module=EEGInception,\n    optimizer=torch.optim.Adam,\n    optimizer__lr=LEARNING_RATE,\n    batch_size=BATCH_SIZE,\n    max_epochs=EPOCH,\n    train_split=ValidSplit(0.2, random_state=SEED),\n    callbacks=[\n        EarlyStopping(monitor=\"valid_loss\", patience=PATIENCE),\n        EpochScoring(\n            scoring=\"accuracy\", on_train=True, name=\"train_acc\", lower_is_better=False\n        ),\n        EpochScoring(\n            scoring=\"accuracy\", on_train=False, name=\"valid_acc\", lower_is_better=False\n        ),\n    ],\n    verbose=VERBOSE,  # Not printing the results for each epoch\n)\n\nclf.initialize()\n\nf_params = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_model.pkl\"\nf_optimizer = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_optim.pkl\"\nf_history = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_history.json\"\n\nclf.load_params(f_params=f_params, f_optimizer=f_optimizer, f_history=f_history)\n\n# Create the pipelines\npipes_pytorch = make_pipeline(clf)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/7f676ecf06fb47e9238e23dd03876176/plot_benchmark_braindecode.ipynb b/docs/_downloads/7f676ecf06fb47e9238e23dd03876176/plot_benchmark_braindecode.ipynb
new file mode 100644
index 00000000..3d9fca61
--- /dev/null
+++ b/docs/_downloads/7f676ecf06fb47e9238e23dd03876176/plot_benchmark_braindecode.ipynb
@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures\nThis example shows how to use MOABB to benchmark a set of Braindecode pipelines (deep learning\narchitectures) on all available datasets.\nFor this example, we will use only 2 datasets to keep the computation time low, but this benchmark is designed\nto easily scale to many datasets.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Igor Carrara <igor.carrara@inria.fr>\n#          Bruno Aristimunha <b.aristimunha@gmail.com>\n#          Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>\n#\n# License: BSD (3-clause)\n\nimport os\n\nimport matplotlib.pyplot as plt\nimport torch\nfrom absl.logging import ERROR, set_verbosity\n\nfrom moabb import benchmark, set_log_level\nfrom moabb.analysis.plotting import score_plot\nfrom moabb.datasets import BNCI2014_001, BNCI2014_004\nfrom moabb.utils import setup_seed\n\n\nset_log_level(\"info\")\n# Avoid output Warning\nset_verbosity(ERROR)\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n\n# Print Information PyTorch\nprint(f\"Torch Version: {torch.__version__}\")\n\n# Set up GPU if it is there\ncuda = torch.cuda.is_available()\ndevice = \"cuda\" if cuda else \"cpu\"\nprint(\"GPU is\", \"AVAILABLE\" if cuda else \"NOT AVAILABLE\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use only 2 subjects from the dataset ``BNCI2014_001`` and ``BNCI2014_004``.\n\n## Running the benchmark\n\nThe benchmark is run using the ``benchmark`` function. You need to specify the\nfolder containing the pipelines, the kind of evaluation, and the paradigm\nto use. By default, the benchmark will use all available datasets for all\nparadigms listed in the pipelines. You could restrict to specific evaluation and\nparadigm using the ``evaluations`` and ``paradigms`` arguments.\n\nTo save computation time, the results are cached. If you want to re-run the\nbenchmark, you can set the ``overwrite`` argument to ``True``.\n\nIt is possible to indicate the folder to cache the results and the one to save\nthe analysis & figures. By default, the results are saved in the ``results``\nfolder, and the analysis & figures are saved in the ``benchmark`` folder.\n\nThis code is implemented to run on CPU. If you're using a GPU, do not use multithreading\n(i.e. set n_jobs=1)\n\nIn order to allow the benchmark function to work with return_epoch=True (Required to use Braindecode(\nwe need to call each pipeline as \"braindecode_xxx...\", with xxx the name of the model to be\nhandled correctly by the benchmark function.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Set up reproducibility of Tensorflow\nsetup_seed(42)\n\n# Restrict this example only to the first two subjects of BNCI2014_001\ndataset = BNCI2014_001()\ndataset2 = BNCI2014_004()\ndataset.subject_list = dataset.subject_list[:2]\ndataset2.subject_list = dataset2.subject_list[:2]\ndatasets = [dataset, dataset2]\n\nresults = benchmark(\n    pipelines=\"./pipelines_braindecode\",\n    evaluations=[\"CrossSession\"],\n    paradigms=[\"LeftRightImagery\"],\n    include_datasets=datasets,\n    results=\"./results/\",\n    overwrite=False,\n    plot=False,\n    output=\"./benchmark/\",\n    n_jobs=-1,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The deep learning architectures implemented in MOABB using Braindecode are:\n\n- Shallow Convolutional Network [1]_\n- Deep Convolutional Network [1]_\n- EEGNetv4 [2]_\n- EEGInception [3]_\n\nBenchmark prints a summary of the results. Detailed results are saved in a\npandas dataframe, and can be used to generate figures. The analysis & figures\nare saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "score_plot(results)\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,\n   Glasstetter, M., Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).\n   [Deep learning with convolutional neural networks for EEG decoding and\n   visualization](https://doi.org/10.1002/hbm.23730).\n   Human brain mapping, 38(11), 5391-5420.\n.. [2] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,\n   Hung, C. P., & Lance, B. J. (2018). [EEGNet: a compact convolutional neural\n   network for EEG-based brain-computer interfaces.](https://doi.org/10.1088/1741-2552/aace8c)\n   Journal of neural engineering, 15(5), 056013.\n.. [3] Santamaria-Vazquez, E., Martinez-Cagigal, V., Vaquerizo-Villar,\n   F., & Hornero, R. (2020). [EEG-inception: A novel deep convolutional neural network\n   for assistive ERP-based brain-computer interfaces.](https://doi.org/10.1109/TNSRE.2020.3048106)\n   IEEE Transactions on Neural Systems and Rehabilitation Engineering\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/82e3e6cb009bb9a9c444c6cff254cea8/plot_cross_session_motor_imagery.py b/docs/_downloads/82e3e6cb009bb9a9c444c6cff254cea8/plot_cross_session_motor_imagery.py
new file mode 100644
index 00000000..33ce516f
--- /dev/null
+++ b/docs/_downloads/82e3e6cb009bb9a9c444c6cff254cea8/plot_cross_session_motor_imagery.py
@@ -0,0 +1,127 @@
+"""
+===========================
+Cross-Session Motor Imagery
+===========================
+
+This example show how to perform a cross session motor imagery analysis on the
+very popular dataset 2a from the BCI competition IV.
+
+We will compare two pipelines :
+
+- CSP+LDA
+- Riemannian Geometry+Logistic Regression
+
+We will use the LeftRightImagery paradigm. This will restrict the analysis
+to two classes (left hand versus right hand) and use AUC as metric.
+
+The cross session evaluation context will evaluate performance using a leave
+one session out cross-validation. For each session in the dataset, a model
+is trained on every other session and performance are evaluated on the current
+session.
+"""
+
+# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
+#          Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
+#
+# License: BSD (3-clause)
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from mne.decoding import CSP
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+#
+# The CSP implementation is based on the MNE implementation. We selected 8 CSP
+# components, as usually done in the literature.
+#
+# The Riemannian geometry pipeline consists in covariance estimation, tangent
+# space mapping and finally a logistic regression for the classification.
+
+pipelines = {}
+
+pipelines["CSP+LDA"] = make_pipeline(CSP(n_components=8), LDA())
+
+pipelines["RG+LR"] = make_pipeline(
+    Covariances(), TangentSpace(), LogisticRegression(solver="lbfgs")
+)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
+# The evaluation will return a DataFrame containing a single AUC score for
+# each subject / session of the dataset, and for each pipeline.
+#
+# Results are saved into the database, so that if you add a new pipeline, it
+# will not run again the evaluation unless a parameter has changed. Results can
+# be overwritten if necessary.
+
+paradigm = LeftRightImagery()
+# Because this is being auto-generated we only use 2 subjects
+dataset = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:2]
+datasets = [dataset]
+overwrite = False  # set to True if we want to overwrite cached results
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
+)
+
+results = evaluation.process(pipelines)
+
+print(results.head())
+
+##############################################################################
+# Plot Results
+# ----------------
+#
+# Here we plot the results. We first make a pointplot with the average
+# performance of each pipeline across session and subjects.
+# The second plot is a paired scatter plot. Each point representing the score
+# of a single session. An algorithm will outperform another is most of the
+# points are in its quadrant.
+
+fig, axes = plt.subplots(1, 2, figsize=[8, 4], sharey=True)
+
+sns.stripplot(
+    data=results,
+    y="score",
+    x="pipeline",
+    ax=axes[0],
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sns.pointplot(data=results, y="score", x="pipeline", ax=axes[0], palette="Set1")
+
+axes[0].set_ylabel("ROC AUC")
+axes[0].set_ylim(0.5, 1)
+
+paired = results.pivot_table(
+    values="score", columns="pipeline", index=["subject", "session"]
+)
+paired = paired.reset_index()
+
+sns.regplot(data=paired, y="RG+LR", x="CSP+LDA", ax=axes[1], fit_reg=False)
+axes[1].plot([0, 1], [0, 1], ls="--", c="k")
+axes[1].set_xlim(0.5, 1)
+
+plt.show()
diff --git a/docs/_downloads/833e0bb5b511f838d653ea6df7213ca7/plot_learning_curve_motor_imagery.py b/docs/_downloads/833e0bb5b511f838d653ea6df7213ca7/plot_learning_curve_motor_imagery.py
new file mode 100644
index 00000000..eaa8491c
--- /dev/null
+++ b/docs/_downloads/833e0bb5b511f838d653ea6df7213ca7/plot_learning_curve_motor_imagery.py
@@ -0,0 +1,121 @@
+"""
+================================================
+Within Session Motor Imagery with Learning Curve
+================================================
+
+This example shows how to perform a within session motor imagery analysis on the
+very popular dataset 2a from the BCI competition IV.
+
+We will compare two pipelines :
+
+- CSP + LDA
+- Riemannian Geometry + Logistic Regression
+
+We will use the LeftRightImagery paradigm. This will restrict the analysis
+to two classes (left- vs right-hand) and use AUC as metric.
+"""
+
+# Original author: Alexandre Barachant <alexandre.barachant@gmail.com>
+# Learning curve modification: Jan Sosulski
+#
+# License: BSD (3-clause)
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from mne.decoding import CSP
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+#
+# The CSP implementation from MNE is used. We selected 8 CSP components, as
+# usually done in the literature.
+#
+# The Riemannian geometry pipeline consists in covariance estimation, tangent
+# space mapping and finally a logistic regression for the classification.
+
+pipelines = {}
+
+pipelines["CSP+LDA"] = make_pipeline(
+    CSP(n_components=8), LDA(solver="lsqr", shrinkage="auto")
+)
+
+pipelines["RG+LR"] = make_pipeline(
+    Covariances(), TangentSpace(), LogisticRegression(solver="lbfgs")
+)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
+# The evaluation will return a DataFrame containing a single AUC score for
+# each subject / session of the dataset, and for each pipeline.
+#
+# Results are saved into the database, so that if you add a new pipeline, it
+# will not run again the evaluation unless a parameter has changed. Results can
+# be overwritten if necessary.
+
+paradigm = LeftRightImagery()
+dataset = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:1]
+datasets = [dataset]
+overwrite = True  # set to True if we want to overwrite cached results
+# Evaluate for a specific number of training samples per class
+data_size = dict(policy="per_class", value=np.array([5, 10, 30, 50]))
+# When the training data is sparse, perform more permutations than when we have a lot of data
+n_perms = np.floor(np.geomspace(20, 2, len(data_size["value"]))).astype(int)
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    suffix="examples",
+    overwrite=overwrite,
+    data_size=data_size,
+    n_perms=n_perms,
+)
+
+results = evaluation.process(pipelines)
+
+print(results.head())
+
+##############################################################################
+# Plot Results
+# ------------
+#
+# We plot the accuracy as a function of the number of training samples, for
+# each pipeline
+
+fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])
+
+n_subs = len(dataset.subject_list)
+
+if n_subs > 1:
+    r = results.groupby(["pipeline", "subject", "data_size"]).mean().reset_index()
+else:
+    r = results
+
+sns.pointplot(data=r, x="data_size", y="score", hue="pipeline", ax=ax, palette="Set1")
+
+errbar_meaning = "subjects" if n_subs > 1 else "permutations"
+title_str = f"Errorbar shows Mean-CI across {errbar_meaning}"
+ax.set_xlabel("Amount of training samples")
+ax.set_ylabel("ROC AUC")
+ax.set_title(title_str)
+fig.tight_layout()
+plt.show()
diff --git a/docs/_downloads/83c876b60654b8a0b9ebaf6714ef3607/plot_select_electrodes_resample.py b/docs/_downloads/83c876b60654b8a0b9ebaf6714ef3607/plot_select_electrodes_resample.py
new file mode 100644
index 00000000..c326b125
--- /dev/null
+++ b/docs/_downloads/83c876b60654b8a0b9ebaf6714ef3607/plot_select_electrodes_resample.py
@@ -0,0 +1,90 @@
+"""
+================================
+Select Electrodes and Resampling
+================================
+
+Within paradigm, it is possible to restrict analysis only to a subset of
+electrodes and to resample to a specific sampling rate. There is also a
+utility function to select common electrodes shared between datasets.
+This tutorial demonstrates how to use this functionality.
+"""
+
+# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
+#
+# License: BSD (3-clause)
+import matplotlib.pyplot as plt
+from mne.decoding import CSP
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.linear_model import LogisticRegression as LR
+from sklearn.pipeline import make_pipeline
+
+import moabb.analysis.plotting as moabb_plt
+from moabb.datasets import BNCI2014_001, Zhou2016
+from moabb.datasets.utils import find_intersecting_channels
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+##############################################################################
+# Datasets
+# --------
+#
+# Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 sessions each
+
+subj = [1, 2]
+datasets = [Zhou2016(), BNCI2014_001()]
+for d in datasets:
+    d.subject_list = subj
+
+##############################################################################
+# Paradigm
+# --------
+#
+# Restrict further analysis to specified channels, here C3, C4, and Cz.
+# Also, use a specific resampling. In this example, all datasets are
+# set to 200 Hz.
+
+paradigm = LeftRightImagery(channels=["C3", "C4", "Cz"], resample=200.0)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# The evaluation is conducted on with CSP+LDA, only on the 3 electrodes, with
+# a sampling rate of 200 Hz.
+
+evaluation = WithinSessionEvaluation(paradigm=paradigm, datasets=datasets)
+csp_lda = make_pipeline(CSP(n_components=2), LDA())
+ts_lr = make_pipeline(
+    Covariances(estimator="oas"), TangentSpace(metric="riemann"), LR(C=1.0)
+)
+results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
+print(results.head())
+
+##############################################################################
+# Electrode Selection
+# -------------------
+#
+# It is possible to select the electrodes that are shared by all datasets
+# using the `find_intersecting_channels` function. Datasets that have 0
+# overlap with others are discarded. It returns the set of common channels,
+# as well as the list of datasets with valid channels.
+
+electrodes, datasets = find_intersecting_channels(datasets)
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, overwrite=True, suffix="resample"
+)
+results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
+print(results.head())
+
+##############################################################################
+# Plot Results
+# ------------
+#
+# Compare the obtained results with the two pipelines, CSP+LDA and logistic
+# regression computed in the tangent space of the covariance matrices.
+
+fig = moabb_plt.paired_plot(results, "csp+lda", "ts+lr")
+plt.show()
diff --git a/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py b/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py
new file mode 100644
index 00000000..2593fd0e
--- /dev/null
+++ b/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py
@@ -0,0 +1,111 @@
+"""
+==============================================
+Load Model (Scikit, Pytorch, Keras) with MOABB
+==============================================
+
+This example shows how to use load the pretrained pipeline in MOABB.
+"""
+
+# Authors: Igor Carrara <igor.carrara@inria.fr>
+#
+# License: BSD (3-clause)
+
+from pickle import load
+
+import keras
+import torch
+from braindecode import EEGClassifier
+from braindecode.models import EEGInception
+from scikeras.wrappers import KerasClassifier
+from sklearn.pipeline import Pipeline, make_pipeline
+from skorch.callbacks import EarlyStopping, EpochScoring
+from skorch.dataset import ValidSplit
+
+from moabb import set_log_level
+from moabb.pipelines.features import StandardScaler_Epoch
+from moabb.utils import setup_seed
+
+
+set_log_level("info")
+
+###############################################################################
+# In this example, we will use the results computed by the following examples
+#
+# - plot_benchmark_
+# - plot_benchmark_braindecode_
+# - plot_benchmark_DL_
+# ---------------------
+
+# Set up reproducibility of Tensorflow and PyTorch
+setup_seed(42)
+
+###############################################################################
+# Loading the Scikit-learn pipelines
+
+with open(
+    "./results/Models_WithinSession/Zhou2016/1/0/CSP + SVM/fitted_model_best.pkl",
+    "rb",
+) as pickle_file:
+    CSP_SVM_Trained = load(pickle_file)
+
+###############################################################################
+# Loading the Keras model
+# We load the single Keras model, if we want we can set in the exact same pipeline.
+
+model_Keras = keras.models.load_model(
+    "./results/Models_WithinSession/BNCI2014-001/1/1E/Keras_DeepConvNet/kerasdeepconvnet_fitted_model_best.h5"
+)
+# Now we need to instantiate a new SciKeras object since we only saved the Keras model
+Keras_DeepConvNet_Trained = KerasClassifier(model_Keras)
+# Create the pipelines
+
+
+pipes_keras = Pipeline(
+    [
+        ("StandardScaler_Epoch", StandardScaler_Epoch),
+        ("Keras_DeepConvNet_Trained", Keras_DeepConvNet_Trained),
+    ]
+)
+
+###############################################################################
+# Loading the PyTorch model
+
+# Hyperparameter
+LEARNING_RATE = 0.0001
+WEIGHT_DECAY = 0
+BATCH_SIZE = 64
+SEED = 42
+VERBOSE = 1
+EPOCH = 2
+PATIENCE = 3
+
+# Define a Skorch classifier
+clf = EEGClassifier(
+    module=EEGInception,
+    optimizer=torch.optim.Adam,
+    optimizer__lr=LEARNING_RATE,
+    batch_size=BATCH_SIZE,
+    max_epochs=EPOCH,
+    train_split=ValidSplit(0.2, random_state=SEED),
+    callbacks=[
+        EarlyStopping(monitor="valid_loss", patience=PATIENCE),
+        EpochScoring(
+            scoring="accuracy", on_train=True, name="train_acc", lower_is_better=False
+        ),
+        EpochScoring(
+            scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False
+        ),
+    ],
+    verbose=VERBOSE,  # Not printing the results for each epoch
+)
+
+clf.initialize()
+
+f_params = "./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_model.pkl"
+f_optimizer = "./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_optim.pkl"
+f_history = "./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_history.json"
+
+clf.load_params(f_params=f_params, f_optimizer=f_optimizer, f_history=f_history)
+
+# Create the pipelines
+pipes_pytorch = make_pipeline(clf)
diff --git a/docs/_downloads/93f59072dcc594921985afeb50d4cf2b/changing_download_directory.py b/docs/_downloads/93f59072dcc594921985afeb50d4cf2b/changing_download_directory.py
new file mode 100644
index 00000000..b42bc2b9
--- /dev/null
+++ b/docs/_downloads/93f59072dcc594921985afeb50d4cf2b/changing_download_directory.py
@@ -0,0 +1,37 @@
+"""
+===========================
+Change Download Directory
+===========================
+
+This is a minimal example to demonstrate how to change the default data
+download directory to a custom path/location. """
+
+# Authors: Divyesh Narayanan <divyesh.narayanan@gmail.com>
+#
+# License: BSD (3-clause)
+
+import os.path as osp
+
+from mne import get_config
+
+from moabb.utils import set_download_dir
+
+
+###############################################################################
+# You can choose to change the download directory to any path of your choice.
+# If the path/folder doesn't exist, it will be created for you.
+
+original_path = get_config("MNE_DATA")
+print(f"The download directory is currently {original_path}")
+new_path = osp.join(osp.expanduser("~"), "mne_data_test")
+set_download_dir(new_path)
+
+###############################################################################
+# You could verify that the MNE config has been changed correctly
+
+check_path = get_config("MNE_DATA")
+print(f"Now the download directory has been changed to {check_path}")
+
+###############################################################################
+# Set the directory back to default location
+set_download_dir(original_path)
diff --git a/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip b/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip
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diff --git a/docs/_downloads/9ab89018120aed26b30f3efdfdef47fe/noplot_bids_conversion.py b/docs/_downloads/9ab89018120aed26b30f3efdfdef47fe/noplot_bids_conversion.py
new file mode 100644
index 00000000..b5bda364
--- /dev/null
+++ b/docs/_downloads/9ab89018120aed26b30f3efdfdef47fe/noplot_bids_conversion.py
@@ -0,0 +1,134 @@
+"""
+===============================
+Convert a MOABB dataset to BIDS
+===============================
+
+The Brain Imaging Data Structure (BIDS) format
+is standard for storing neuroimaging data.
+It follows fixed principles to facilitate the
+sharing of neuroimaging data between researchers.
+
+The MOABB library allows to convert any MOABB dataset to
+BIDS [1]_ and [2]_.
+
+In this example, we will convert the AlexMI dataset to BIDS using the
+option ``cache_config=dict(path=temp_dir, save_raw=True)`` of the ``get_data``
+method from the dataset object.
+
+This will automatically save the raw data in the BIDS format and allow to use
+a cache for the next time the dataset is used.
+
+We will use the AlexMI dataset [3]_, one of the smallest in
+people and one that can be downloaded quickly.
+"""
+
+# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>
+#
+# License: BSD (3-clause)
+
+import shutil
+import tempfile
+from pathlib import Path
+
+import mne
+
+from moabb import set_log_level
+from moabb.datasets import AlexMI
+
+
+set_log_level("info")
+
+###############################################################################
+# Basic usage
+# -----------
+#
+# Here, we will save the BIDS version of the dataset in a temporary folder
+temp_dir = Path(tempfile.mkdtemp())
+# The conversion of any MOABB dataset to a BIDS-compliant structure can be done
+# by simply calling its ``get_data`` method and using the ``cache_config``
+# parameter. This parameter is a dictionary.
+dataset = AlexMI()
+_ = dataset.get_data(cache_config=dict(path=temp_dir, save_raw=True))
+
+
+###############################################################################
+# Before / after folder structure
+# -----------------------------
+#
+# To investigate what was saved, we will first define a function to print
+# the folder structure of a given path:
+def print_tree(p: Path, last=True, header=""):
+    elbow = "└──"
+    pipe = "│  "
+    tee = "├──"
+    blank = "   "
+    print(header + (elbow if last else tee) + p.name)
+    if p.is_dir():
+        children = list(p.iterdir())
+        for i, c in enumerate(children):
+            print_tree(
+                c, header=header + (blank if last else pipe), last=i == len(children) - 1
+            )
+
+
+###############################################################################
+# Now, we will retrieve the location of the original dataset. It is stored
+# in the MNE data directory, which can be found with the ``"MNE_DATA"`` key:
+mne_data = Path(mne.get_config("MNE_DATA"))
+print(f"MNE data directory: {mne_data}")
+
+###############################################################################
+# Now, we can print the folder structure of the original dataset:
+print("Before conversion:")
+print_tree(mne_data / "MNE-alexeeg-data")
+
+###############################################################################
+# As we can see, before conversion, all the data (i.e. from all subjects,
+# sessions and runs) is stored in a single folder. This follows no particular
+# standard and can vary from one dataset to another.
+#
+# After conversion, the data is stored in a BIDS-compliant way:
+print("After conversion:")
+print_tree(temp_dir / "MNE-BIDS-alexandre-motor-imagery")
+
+###############################################################################
+# In the BIDS version of our dataset, the raw files are saved in EDF.
+# The data is organized in a hierarchy of folders,
+# starting with the subjects, then the sessions, and then the runs. Metadata
+# files are stored to describe the data. For more details on the BIDS
+# structure, please refer to the `BIDS website <https://bids.neuroimaging.io>`_
+# and the `BIDS spec <https://bids-specification.readthedocs.io/en/stable/>`_.
+#
+# Under the hood, saving datasets to BIDS is done through the caching system
+# of MOABB. Only raw EEG files are officially supported by the BIDS
+# specification.
+# However, MOABB's caching mechanism also offers the possibility to save
+# the data in a pseudo-BIDS after different preprocessing steps.
+# In particular, we can save :class:`mne.Epochs` and ``np.ndarray`` objects.
+# For more details on the caching system,
+# please refer to the tutorial :doc:`./plot_disk_cache`.
+#
+# Cleanup
+# -------
+#
+# Finally, we can delete the temporary folder:
+shutil.rmtree(temp_dir)
+
+###############################################################################
+# References
+# -----------
+#
+# .. [1] Pernet, C.R., Appelhoff, S., Gorgolewski, K.J. et al. EEG-BIDS,
+#        An extension to the brain imaging data structure for
+#        electroencephalography. Sci Data 6, 103 (2019).
+#        https://doi.org/10.1038/s41597-019-0104-8
+#
+# .. [2] Appelhoff et al., (2019). MNE-BIDS: Organizing electrophysiological
+#        data into the BIDS format and facilitating their analysis.
+#        Journal of Open Source Software, 4(44), 1896,
+#        https://doi.org/10.21105/joss.01896
+#
+# .. [3] Barachant, A., 2012. Commande robuste d'un effecteur par une
+#        interface cerveau machine EEG asynchrone (Doctoral dissertation,
+#        Université de Grenoble).
+#        https://tel.archives-ouvertes.fr/tel-01196752
diff --git a/docs/_downloads/9b872d163c45f60eb451b1869667203a/plot_explore_paradigm.py b/docs/_downloads/9b872d163c45f60eb451b1869667203a/plot_explore_paradigm.py
new file mode 100644
index 00000000..ce3b6667
--- /dev/null
+++ b/docs/_downloads/9b872d163c45f60eb451b1869667203a/plot_explore_paradigm.py
@@ -0,0 +1,147 @@
+"""
+=======================
+Explore Paradigm Object
+=======================
+
+A paradigm defines how the raw data will be converted to trials ready
+to be processed by a decoding algorithm. This is a function of the paradigm
+used, i.e. in motor imagery one can have two-class, multi-class,
+or continuous paradigms; similarly, different preprocessing is necessary
+for ERP vs ERD paradigms.
+
+A paradigm also defines the appropriate evaluation metric, for example AUC
+for binary classification problems, accuracy for multiclass, or kappa
+coefficients for continuous paradigms.
+
+This tutorial explores the paradigm object, with 3 examples of paradigm :
+     - MotorImagery
+     - FilterBankMotorImagery
+     - LeftRightImagery
+
+"""
+
+# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
+#          Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
+#
+# License: BSD (3-clause)
+
+import numpy as np
+
+from moabb.datasets import BNCI2014_001
+from moabb.paradigms import FilterBankMotorImagery, LeftRightImagery, MotorImagery
+
+
+print(__doc__)
+
+###############################################################################
+# MotorImagery
+# -----------------
+#
+# First, let's take an example of the MotorImagery paradigm.
+
+paradigm = MotorImagery(n_classes=4)
+
+print(paradigm.__doc__)
+
+###############################################################################
+# The function `get_data` allow you to access preprocessed data from a dataset.
+# this function will return 3 objects. A numpy array containing the
+# preprocessed EEG data, the labels, and a dataframe with metadata.
+
+print(paradigm.get_data.__doc__)
+
+###############################################################################
+# Lets take the example of the BNCI2014_001 dataset, known as the dataset IIa
+# from the BCI competition IV. We will load the data from the subject 1.
+# When calling `get_data`, the paradigm will retrieve the data from the
+# specified list of subjects, apply preprocessing (by default, a bandpass
+# between 7 and 35 Hz), epoch the data (with interval specified by the dataset,
+# unless superseded by the paradigm) and return the corresponding objects.
+
+dataset = BNCI2014_001()
+subjects = [1]
+
+X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)
+
+###############################################################################
+# The epoched data is a 3D array, with epochs on the first dimension (here
+# 576 trials), channels on the second (22 channels) and time sample on the last
+# one.
+
+print(X.shape)
+
+###############################################################################
+# Labels contains the labels corresponding to each trial. in the case of this
+# dataset, we have the 4 types of motor imagery that was performed.
+
+print(np.unique(y))
+
+###############################################################################
+# Metadata have at least 3 columns: subject, session and run.
+#
+# - subject is the subject id of the corresponding trial
+# - session is the session id. A session denotes a recording made without
+#   removing the EEG cap.
+# - run is the individual continuous recording made during a session. A session
+#   may or may not contain multiple runs.
+#
+
+print(metadata.head())
+
+###############################################################################
+# For this data, we have one subject, 2 sessions (2 different recording days)
+# and 6 runs per session.
+
+print(metadata.describe(include="all"))
+
+###############################################################################
+# Paradigm objects can also return the list of all dataset compatible. Here
+# it will return the list all the imagery datasets from the MOABB.
+
+compatible_datasets = paradigm.datasets
+print([dataset.code for dataset in compatible_datasets])
+
+###############################################################################
+# FilterBank MotorImagery
+# -----------------------
+#
+# FilterBankMotorImagery is the same paradigm, but with a different
+# preprocessing. In this case, it applies a bank of 6 bandpass filter on the data
+# before concatenating the output.
+
+paradigm = FilterBankMotorImagery()
+
+print(paradigm.__doc__)
+
+###############################################################################
+# Therefore, the output X is a 4D array, with trial x channel x time x filter
+
+X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)
+
+print(X.shape)
+
+###############################################################################
+# LeftRight MotorImagery
+# ----------------------
+#
+# LeftRightImagery is a variation over the BaseMotorImagery paradigm,
+# restricted to left- and right-hand events.
+
+paradigm = LeftRightImagery()
+
+print(paradigm.__doc__)
+
+###############################################################################
+# The compatible dataset list is a subset of motor imagery dataset that
+# contains at least left and right hand events.
+
+compatible_datasets = paradigm.datasets
+print([dataset.code for dataset in compatible_datasets])
+
+###############################################################################
+# So if we apply this to our original dataset, it will only return trials
+# corresponding to left- and right-hand motor imagination.
+
+X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)
+
+print(np.unique(y))
diff --git a/docs/_downloads/9bb96dc7985cb9e3189360966fe90306/tutorial_1_simple_example_motor_imagery.ipynb b/docs/_downloads/9bb96dc7985cb9e3189360966fe90306/tutorial_1_simple_example_motor_imagery.ipynb
new file mode 100644
index 00000000..b29511c7
--- /dev/null
+++ b/docs/_downloads/9bb96dc7985cb9e3189360966fe90306/tutorial_1_simple_example_motor_imagery.ipynb
@@ -0,0 +1,288 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Tutorial 1: Simple Motor Imagery\n\nIn this example, we will go through all the steps to make a simple BCI\nclassification task, downloading a dataset and using a standard classifier. We\nchoose the dataset 2a from BCI Competition IV, a motor imagery task. We will\nuse a CSP to enhance the signal-to-noise ratio of the EEG epochs and a LDA to\nclassify these signals.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier\n#\n# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nmoabb.set_log_level(\"info\")\nwarnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiating Dataset\n\nThe first thing to do is to instantiate the dataset that we want to analyze.\nMOABB has a list of many different datasets, each one containing all the\nnecessary information for describing them, such as the number of subjects,\nsize of trials, names of classes, etc.\n\nThe dataset class has methods for:\n\n- downloading its files from some online source (e.g. Zenodo)\n- importing the data from the files in whatever extension they might be\n  (like .mat, .gdf, etc.) and instantiate a Raw object from the MNE package\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = BNCI2014_001()\ndataset.subject_list = [1, 2, 3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Accessing EEG Recording\n\nAs an example, we may access the EEG recording from a given session and a\ngiven run as follows:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sessions = dataset.get_data(subjects=[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This returns a MNE Raw object that can be manipulated. This might be enough\nfor some users, since the pre-processing and epoching steps can be easily\ndone via MNE. However, to conduct an assessment of several classifiers on\nmultiple subjects, MOABB ends up being a more appropriate option.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subject = 1\nsession_name = \"0train\"\nrun_name = \"0\"\nraw = sessions[subject][session_name][run_name]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Choosing a Paradigm\n\nOnce we have instantiated a dataset, we have to choose a paradigm. This\nobject is responsible for filtering the data, epoching it, and extracting\nthe labels for each epoch. Note that each dataset comes with the names of\nthe paradigms to which it might be associated. It would not make sense to\nprocess a P300 dataset with a MI paradigm object.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(dataset.paradigm)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For the example below, we will consider the paradigm associated to\nleft-hand/right-hand motor imagery task, but there are other options in\nMOABB for motor imagery, P300 or SSVEP.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We may check the list of all datasets available in MOABB for using with this\nparadigm (note that BNCI2014_001 is in it)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(paradigm.datasets)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The data from a list of subjects could be preprocessed and return as a 3D\nnumpy array `X`, follow a scikit-like format with the associated `labels`.\nThe `meta` object contains all information regarding the subject, the\nsession and the run associated to each trial.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipeline\n\nOur goal is to evaluate the performance of a given classification pipeline\n(or several of them) when it is applied to the epochs from the previously\nchosen dataset. We will consider a very simple classification pipeline in\nwhich the dimension of the epochs are reduced via a CSP step and then\nclassified via a linear discriminant analysis.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipeline = make_pipeline(CSP(n_components=8), LDA())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nTo evaluate the score of this pipeline, we use the `evaluation` class. When\ninstantiating it, we say which paradigm we want to consider, a list with the\ndatasets to analyze, and whether the scores should be recalculated each time\nwe run the evaluation or if MOABB should create a cache file.\n\nNote that there are different ways of evaluating a classifier; in this\nexample, we choose `WithinSessionEvaluation`, which consists of doing a\ncross-validation procedure where the training and testing partitions are from\nthe same recording session of the dataset. We could have used\n`CrossSessionEvaluation`, which takes all but one session as training\npartition and the remaining one as testing partition.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "evaluation = WithinSessionEvaluation(\n    paradigm=paradigm,\n    datasets=[dataset],\n    overwrite=True,\n    hdf5_path=None,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We obtain the results in the form of a pandas dataframe\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results = evaluation.process({\"csp+lda\": pipeline})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The results are stored in locally, to avoid recomputing the results each time.\nIt is saved in `hdf5_path` if defined or in ~/mne_data/results  otherwise.\nTo export the results in CSV:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results.to_csv(\"./results_part2-1.csv\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To load previously obtained results saved in CSV\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results = pd.read_csv(\"./results_part2-1.csv\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting Results\n\nWe create a figure with the seaborn package comparing the classification\nscore for each subject on each session. Note that the 'subject' field from\nthe `results` is given in terms of integers, but seaborn accepts only\nstrings for its labeling. This is why we create the field 'subj'.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8, 7))\nresults[\"subj\"] = results[\"subject\"].apply(str)\nsns.barplot(\n    x=\"score\", y=\"subj\", hue=\"session\", data=results, orient=\"h\", palette=\"viridis\", ax=ax\n)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/9d4b12baed30d0e2fe09c8116ace859d/plot_learning_curve_p300.py b/docs/_downloads/9d4b12baed30d0e2fe09c8116ace859d/plot_learning_curve_p300.py
new file mode 100644
index 00000000..cea40639
--- /dev/null
+++ b/docs/_downloads/9d4b12baed30d0e2fe09c8116ace859d/plot_learning_curve_p300.py
@@ -0,0 +1,126 @@
+"""
+=======================================
+Within Session P300 with Learning Curve
+=======================================
+
+This example shows how to perform a within session analysis while also
+creating learning curves for a P300 dataset.
+Additionally, we will evaluate external code. Make sure to have tdlda installed, which
+can be found in requirements_external.txt
+
+We will compare two pipelines :
+
+- Riemannian geometry with Linear Discriminant Analysis
+- XDAWN and Linear Discriminant Analysis
+
+We will use the P300 paradigm, which uses the AUC as metric.
+"""
+
+# Authors: Jan Sosulski
+#
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from mne.decoding import Vectorizer
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.spatialfilters import Xdawn
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_009
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import P300
+
+
+# getting rid of the warnings about the future (on s'en fout !)
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+
+moabb.set_log_level("info")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+processing_sampling_rate = 128
+pipelines = {}
+
+##############################################################################
+# We have to do this because the classes are called 'Target' and 'NonTarget'
+# but the evaluation function uses a LabelEncoder, transforming them
+# to 0 and 1
+labels_dict = {"Target": 1, "NonTarget": 0}
+
+# Riemannian geometry based classification
+pipelines["RG+LDA"] = make_pipeline(
+    XdawnCovariances(nfilter=5, estimator="lwf", xdawn_estimator="scm"),
+    TangentSpace(),
+    LDA(solver="lsqr", shrinkage="auto"),
+)
+
+pipelines["Xdw+LDA"] = make_pipeline(
+    Xdawn(nfilter=2, estimator="scm"), Vectorizer(), LDA(solver="lsqr", shrinkage="auto")
+)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# We define the paradigm (P300) and use all three datasets available for it.
+# The evaluation will return a DataFrame containing AUCs for each permutation
+# and dataset size.
+
+paradigm = P300(resample=processing_sampling_rate)
+dataset = BNCI2014_009()
+# Remove the slicing of the subject list to evaluate multiple subjects
+dataset.subject_list = dataset.subject_list[1:2]
+datasets = [dataset]
+overwrite = True  # set to True if we want to overwrite cached results
+data_size = dict(policy="ratio", value=np.geomspace(0.02, 1, 4))
+# When the training data is sparse, perform more permutations than when we have a lot of data
+n_perms = np.floor(np.geomspace(20, 2, len(data_size["value"]))).astype(int)
+# Guarantee reproducibility
+np.random.seed(7536298)
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    data_size=data_size,
+    n_perms=n_perms,
+    suffix="examples_lr",
+    overwrite=overwrite,
+)
+
+results = evaluation.process(pipelines)
+
+##############################################################################
+# Plot Results
+# ------------
+#
+# We plot the accuracy as a function of the number of training samples, for
+# each pipeline
+
+fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])
+
+n_subs = len(dataset.subject_list)
+
+if n_subs > 1:
+    r = results.groupby(["pipeline", "subject", "data_size"]).mean().reset_index()
+else:
+    r = results
+
+sns.pointplot(data=r, x="data_size", y="score", hue="pipeline", ax=ax, palette="Set1")
+
+errbar_meaning = "subjects" if n_subs > 1 else "permutations"
+title_str = f"Errorbar shows Mean-CI across {errbar_meaning}"
+ax.set_xlabel("Amount of training samples")
+ax.set_ylabel("ROC AUC")
+ax.set_title(title_str)
+fig.tight_layout()
+plt.show()
diff --git a/docs/_downloads/a1721df1b7d19e3e027162c49fa733d4/tutorial_2_using_mulitple_datasets.py b/docs/_downloads/a1721df1b7d19e3e027162c49fa733d4/tutorial_2_using_mulitple_datasets.py
new file mode 100644
index 00000000..41228092
--- /dev/null
+++ b/docs/_downloads/a1721df1b7d19e3e027162c49fa733d4/tutorial_2_using_mulitple_datasets.py
@@ -0,0 +1,76 @@
+"""
+===================================
+Tutorial 2: Using multiple datasets
+===================================
+
+We extend the previous example to a case where we want to analyze the score of
+a classifier with three different MI datasets instead of just one. As before,
+we begin by importing all relevant libraries.
+"""
+
+# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
+#
+# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
+
+import warnings
+
+import matplotlib.pyplot as plt
+import mne
+import seaborn as sns
+from mne.decoding import CSP
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_001, Zhou2016
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+moabb.set_log_level("info")
+mne.set_log_level("CRITICAL")
+warnings.filterwarnings("ignore")
+
+##############################################################################
+# Initializing Datasets
+# ---------------------
+#
+# We instantiate the two different datasets that follow the MI paradigm
+# (with left-hand/right-hand classes) but were recorded with different number
+# of electrodes, different number of trials, etc.
+
+datasets = [Zhou2016(), BNCI2014_001()]
+subj = [1, 2, 3]
+for d in datasets:
+    d.subject_list = subj
+
+##############################################################################
+# The following lines go exactly as in the previous example, where we end up
+# obtaining a pandas dataframe containing the results of the evaluation. We
+# could set `overwrite` to False to cache the results, avoiding to restart all
+# the evaluation from scratch if a problem occurs.
+paradigm = LeftRightImagery()
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, overwrite=False
+)
+pipeline = make_pipeline(CSP(n_components=8), LDA())
+results = evaluation.process({"csp+lda": pipeline})
+
+##############################################################################
+# Plotting Results
+# ----------------
+#
+# We plot the results using the seaborn library. Note how easy it
+# is to plot the results from the three datasets with just one line.
+
+results["subj"] = [str(resi).zfill(2) for resi in results["subject"]]
+g = sns.catplot(
+    kind="bar",
+    x="score",
+    y="subj",
+    col="dataset",
+    data=results,
+    orient="h",
+    palette="viridis",
+)
+plt.show()
diff --git a/docs/_downloads/b129d24bc810ae3a961d5bdcb7413828/plot_benchmark_grid_search.py b/docs/_downloads/b129d24bc810ae3a961d5bdcb7413828/plot_benchmark_grid_search.py
new file mode 100644
index 00000000..b7d91078
--- /dev/null
+++ b/docs/_downloads/b129d24bc810ae3a961d5bdcb7413828/plot_benchmark_grid_search.py
@@ -0,0 +1,67 @@
+"""
+========================================
+Benchmarking with MOABB with Grid Search
+========================================
+
+This example shows how to use MOABB to benchmark a set of pipelines
+on all available datasets. In particular we run the Gridsearch to select the best hyperparameter of some pipelines
+and save the gridsearch.
+For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.
+"""
+
+# Authors: Igor Carrara <igor.carrara@inria.fr>
+#
+# License: BSD (3-clause)
+
+import matplotlib.pyplot as plt
+
+from moabb import benchmark, set_log_level
+from moabb.analysis.plotting import score_plot
+
+
+set_log_level("info")
+
+###############################################################################
+# In this example, we will use only the dataset 'Zhou 2016'.
+#
+# Running the benchmark
+# ---------------------
+#
+# The benchmark is run using the ``benchmark`` function. You need to specify the
+# folder containing the pipelines to use, the kind of evaluation and the paradigm
+# to use. By default, the benchmark will use all available datasets for all
+# paradigms listed in the pipelines. You could restrict to specific evaluation and
+# paradigm using the ``evaluations`` and ``paradigms`` arguments.
+#
+# To save computation time, the results are cached. If you want to re-run the
+# benchmark, you can set the ``overwrite`` argument to ``True``.
+#
+# It is possible to indicate the folder to cache the results and the one to save
+# the analysis & figures. By default, the results are saved in the ``results``
+# folder, and the analysis & figures are saved in the ``benchmark`` folder.
+
+# In the results folder we will save the gridsearch evaluation
+# When write the pipeline in ylm file we need to specify the parameter that we want to test, in format
+# pipeline-name__estimator-name_parameter. Note that pipeline and estimator names MUST
+# be in lower case (no capital letters allowed).
+# If the grid search is already implemented it will load the previous results
+
+results = benchmark(
+    pipelines="./pipelines_grid/",
+    evaluations=["WithinSession"],
+    paradigms=["LeftRightImagery"],
+    include_datasets=["Zhou2016"],
+    results="./results/",
+    overwrite=False,
+    plot=False,
+    output="./benchmark/",
+)
+
+###############################################################################
+# Benchmark prints a summary of the results. Detailed results are saved in a
+# pandas dataframe, and can be used to generate figures. The analysis & figures
+# are saved in the ``benchmark`` folder.
+
+score_plot(results)
+plt.show()
diff --git a/docs/_downloads/b298e4b36adf2bb5cb6f65682eec852f/plot_grid_search_withinsession.py b/docs/_downloads/b298e4b36adf2bb5cb6f65682eec852f/plot_grid_search_withinsession.py
new file mode 100644
index 00000000..a2f1aefb
--- /dev/null
+++ b/docs/_downloads/b298e4b36adf2bb5cb6f65682eec852f/plot_grid_search_withinsession.py
@@ -0,0 +1,175 @@
+"""
+============================
+GridSearch within a session
+============================
+
+This example demonstrates how to make a model selection in pipelines
+for finding the best model parameter, using grid search. Two models
+are compared, one "vanilla" model with model tuned via grid search.
+"""
+
+import os
+from pickle import load
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import Pipeline
+
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import MotorImagery
+
+
+# Initialize parameter for the Band Pass filter
+fmin = 8
+fmax = 35
+tmax = None
+
+# Select the Subject
+subjects = [1]
+# Load the dataset
+dataset = BNCI2014_001()
+
+events = ["right_hand", "left_hand"]
+
+paradigm = MotorImagery(
+    events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmax=tmax
+)
+
+# Create a path and folder for every subject
+path = os.path.join(str("Results"))
+os.makedirs(path, exist_ok=True)
+
+##############################################################################
+# Create the Pipelines
+# --------------------
+# Two pipelines implementing elastic net classifiers, one using a fixed
+# l1_ratio ("VanillaEN") and the other using a range of values to select
+# l1_ratio ("GridSearchEN")
+
+pipelines = {}
+pipelines["VanillaEN"] = Pipeline(
+    steps=[
+        ("Covariances", Covariances("cov")),
+        ("Tangent_Space", TangentSpace(metric="riemann")),
+        (
+            "LogistReg",
+            LogisticRegression(
+                penalty="elasticnet",
+                l1_ratio=0.75,
+                intercept_scaling=1000.0,
+                solver="saga",
+                max_iter=1000,
+            ),
+        ),
+    ]
+)
+
+pipelines["GridSearchEN"] = Pipeline(
+    steps=[
+        ("Covariances", Covariances("cov")),
+        ("Tangent_Space", TangentSpace(metric="riemann")),
+        (
+            "LogistReg",
+            LogisticRegression(
+                penalty="elasticnet",
+                l1_ratio=0.70,
+                intercept_scaling=1000.0,
+                solver="saga",
+                max_iter=1000,
+            ),
+        ),
+    ]
+)
+
+##############################################################################
+# The search space for parameters is defined as a dictionary, specifying the
+# name of the estimator and the parameter name as a key.
+
+param_grid = {}
+param_grid["GridSearchEN"] = {
+    "LogistReg__l1_ratio": [0.15, 0.30, 0.45, 0.60, 0.75],
+}
+
+##############################################################################
+# Running the Evaluation
+# ----------------------
+# If a param_grid is specified during process, the specified pipelines will
+# automatically be run with a grid search.
+
+dataset.subject_list = dataset.subject_list[:1]
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm,
+    datasets=dataset,
+    overwrite=True,
+    random_state=42,
+    hdf5_path=path,
+    n_jobs=-1,
+    save_model=True,
+)
+result = evaluation.process(pipelines, param_grid)
+
+#####################################################################
+# Plot Results
+# ------------
+# The grid search allows to find better parameter during the
+# evaluation, leading to better accuracy results.
+
+fig, axes = plt.subplots(1, 1, figsize=[8, 5], sharey=True)
+
+sns.stripplot(
+    data=result,
+    y="score",
+    x="pipeline",
+    ax=axes,
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sns.pointplot(data=result, y="score", x="pipeline", ax=axes, palette="Set1")
+axes.set_ylabel("ROC AUC")
+
+##########################################################
+# Load Best Model Parameter
+# -------------------------
+# The best model are automatically saved in a pickle file, in the
+# results directory. It is possible to load those model for each
+# dataset, subject and session. Here, we could see that the grid
+# search found a l1_ratio that is different from the baseline
+# value.
+
+with open(
+    "./Results/Models_WithinSession/BNCI2014-001/1/1test/GridSearchEN/fitted_model_best.pkl",
+    "rb",
+) as pickle_file:
+    GridSearchEN_Session_E = load(pickle_file)
+
+print(
+    "Best Parameter l1_ratio Session_E GridSearchEN ",
+    GridSearchEN_Session_E.best_params_["LogistReg__l1_ratio"],
+)
+
+print(
+    "Best Parameter l1_ratio Session_E VanillaEN: ",
+    pipelines["VanillaEN"].steps[2][1].l1_ratio,
+)
+
+with open(
+    "./Results/Models_WithinSession/BNCI2014-001/1/0train/GridSearchEN/fitted_model_best.pkl",
+    "rb",
+) as pickle_file:
+    GridSearchEN_Session_T = load(pickle_file)
+
+print(
+    "Best Parameter l1_ratio Session_T GridSearchEN ",
+    GridSearchEN_Session_T.best_params_["LogistReg__l1_ratio"],
+)
+
+print(
+    "Best Parameter l1_ratio Session_T VanillaEN: ",
+    pipelines["VanillaEN"].steps[2][1].l1_ratio,
+)
diff --git a/docs/_downloads/b446bb6bcfa12c42d029c4259163f668/tutorial_1_simple_example_motor_imagery.py b/docs/_downloads/b446bb6bcfa12c42d029c4259163f668/tutorial_1_simple_example_motor_imagery.py
new file mode 100644
index 00000000..7d564374
--- /dev/null
+++ b/docs/_downloads/b446bb6bcfa12c42d029c4259163f668/tutorial_1_simple_example_motor_imagery.py
@@ -0,0 +1,171 @@
+"""
+================================
+Tutorial 1: Simple Motor Imagery
+================================
+
+In this example, we will go through all the steps to make a simple BCI
+classification task, downloading a dataset and using a standard classifier. We
+choose the dataset 2a from BCI Competition IV, a motor imagery task. We will
+use a CSP to enhance the signal-to-noise ratio of the EEG epochs and a LDA to
+classify these signals.
+"""
+
+# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
+#
+# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
+
+import warnings
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from mne.decoding import CSP
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+moabb.set_log_level("info")
+warnings.filterwarnings("ignore")
+
+##############################################################################
+# Instantiating Dataset
+# ---------------------
+#
+# The first thing to do is to instantiate the dataset that we want to analyze.
+# MOABB has a list of many different datasets, each one containing all the
+# necessary information for describing them, such as the number of subjects,
+# size of trials, names of classes, etc.
+#
+# The dataset class has methods for:
+#
+# - downloading its files from some online source (e.g. Zenodo)
+# - importing the data from the files in whatever extension they might be
+#   (like .mat, .gdf, etc.) and instantiate a Raw object from the MNE package
+
+dataset = BNCI2014_001()
+dataset.subject_list = [1, 2, 3]
+
+##############################################################################
+# Accessing EEG Recording
+# -----------------------
+#
+# As an example, we may access the EEG recording from a given session and a
+# given run as follows:
+
+sessions = dataset.get_data(subjects=[1])
+
+##############################################################################
+# This returns a MNE Raw object that can be manipulated. This might be enough
+# for some users, since the pre-processing and epoching steps can be easily
+# done via MNE. However, to conduct an assessment of several classifiers on
+# multiple subjects, MOABB ends up being a more appropriate option.
+
+subject = 1
+session_name = "0train"
+run_name = "0"
+raw = sessions[subject][session_name][run_name]
+
+##############################################################################
+# Choosing a Paradigm
+# -------------------
+#
+# Once we have instantiated a dataset, we have to choose a paradigm. This
+# object is responsible for filtering the data, epoching it, and extracting
+# the labels for each epoch. Note that each dataset comes with the names of
+# the paradigms to which it might be associated. It would not make sense to
+# process a P300 dataset with a MI paradigm object.
+
+print(dataset.paradigm)
+
+##############################################################################
+# For the example below, we will consider the paradigm associated to
+# left-hand/right-hand motor imagery task, but there are other options in
+# MOABB for motor imagery, P300 or SSVEP.
+
+paradigm = LeftRightImagery()
+
+##############################################################################
+# We may check the list of all datasets available in MOABB for using with this
+# paradigm (note that BNCI2014_001 is in it)
+
+print(paradigm.datasets)
+
+##############################################################################
+# The data from a list of subjects could be preprocessed and return as a 3D
+# numpy array `X`, follow a scikit-like format with the associated `labels`.
+# The `meta` object contains all information regarding the subject, the
+# session and the run associated to each trial.
+X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[1])
+
+##############################################################################
+# Create Pipeline
+# ---------------
+#
+# Our goal is to evaluate the performance of a given classification pipeline
+# (or several of them) when it is applied to the epochs from the previously
+# chosen dataset. We will consider a very simple classification pipeline in
+# which the dimension of the epochs are reduced via a CSP step and then
+# classified via a linear discriminant analysis.
+
+pipeline = make_pipeline(CSP(n_components=8), LDA())
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# To evaluate the score of this pipeline, we use the `evaluation` class. When
+# instantiating it, we say which paradigm we want to consider, a list with the
+# datasets to analyze, and whether the scores should be recalculated each time
+# we run the evaluation or if MOABB should create a cache file.
+#
+# Note that there are different ways of evaluating a classifier; in this
+# example, we choose `WithinSessionEvaluation`, which consists of doing a
+# cross-validation procedure where the training and testing partitions are from
+# the same recording session of the dataset. We could have used
+# `CrossSessionEvaluation`, which takes all but one session as training
+# partition and the remaining one as testing partition.
+
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm,
+    datasets=[dataset],
+    overwrite=True,
+    hdf5_path=None,
+)
+
+##############################################################################
+# We obtain the results in the form of a pandas dataframe
+
+results = evaluation.process({"csp+lda": pipeline})
+
+##############################################################################
+# The results are stored in locally, to avoid recomputing the results each time.
+# It is saved in `hdf5_path` if defined or in ~/mne_data/results  otherwise.
+# To export the results in CSV:
+
+results.to_csv("./results_part2-1.csv")
+
+##############################################################################
+# To load previously obtained results saved in CSV
+
+results = pd.read_csv("./results_part2-1.csv")
+
+##############################################################################
+# Plotting Results
+# ----------------
+#
+# We create a figure with the seaborn package comparing the classification
+# score for each subject on each session. Note that the 'subject' field from
+# the `results` is given in terms of integers, but seaborn accepts only
+# strings for its labeling. This is why we create the field 'subj'.
+
+fig, ax = plt.subplots(figsize=(8, 7))
+results["subj"] = results["subject"].apply(str)
+sns.barplot(
+    x="score", y="subj", hue="session", data=results, orient="h", palette="viridis", ax=ax
+)
+plt.show()
diff --git a/docs/_downloads/b766ff8fcb1665a3edb7ffee945fcb2d/noplot_phmd_ml_spectrum.py b/docs/_downloads/b766ff8fcb1665a3edb7ffee945fcb2d/noplot_phmd_ml_spectrum.py
new file mode 100644
index 00000000..74613870
--- /dev/null
+++ b/docs/_downloads/b766ff8fcb1665a3edb7ffee945fcb2d/noplot_phmd_ml_spectrum.py
@@ -0,0 +1,71 @@
+"""
+================================
+Spectral analysis of the trials
+================================
+
+This example demonstrates how to perform spectral
+analysis on epochs extracted from a specific subject
+within the :class:`moabb.datasets.Cattan2019_PHMD`  dataset.
+
+"""
+
+# Authors: Pedro Rodrigues <pedro.rodrigues01@gmail.com>
+# Modified by: Gregoire Cattan <gcattan@hotmail.fr>
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from moabb.datasets import Cattan2019_PHMD
+from moabb.paradigms import RestingStateToP300Adapter
+
+
+warnings.filterwarnings("ignore")
+
+###############################################################################
+# Initialization
+# ---------------
+#
+# 1) Specify the channel and subject to compute the power spectrum.
+# 2) Create an instance of the :class:`moabb.datasets.Cattan2019_PHMD` dataset.
+# 3) Create an instance of the :class:`moabb.paradigms.RestingStateToP300Adapter`  paradigm.
+#    By default, the data is filtered between 1-35 Hz,
+#    and epochs are extracted from 10 to 50 seconds after event tagging.
+
+# Select channel and subject for the remaining of the example.
+channel = "Cz"
+subject = 1
+
+dataset = Cattan2019_PHMD()
+events = ["on", "off"]
+paradigm = RestingStateToP300Adapter(events=events, channels=[channel])
+
+###############################################################################
+# Estimate Power Spectral Density
+# ---------------
+# 1) Obtain the epochs for the specified subject.
+# 2) Use Welch's method to estimate the power spectral density.
+
+f, S, _, y = paradigm.psd(subject, dataset)
+
+###############################################################################
+# Display of the data
+# ---------------
+#
+# Plot the averaged Power Spectral Density (PSD) for each label condition,
+# using the selected channel specified at the beginning of the script.
+
+fig, ax = plt.subplots(facecolor="white", figsize=(8.2, 5.1))
+for condition in events:
+    mean_power = np.mean(S[y == condition], axis=0).flatten()
+    ax.plot(f, 10 * np.log10(mean_power), label=condition)
+
+ax.set_xlim(paradigm.fmin, paradigm.fmax)
+ax.set_ylim(100, 135)
+ax.set_ylabel("Spectrum Magnitude (dB)", fontsize=14)
+ax.set_xlabel("Frequency (Hz)", fontsize=14)
+ax.set_title("PSD for Channel " + channel, fontsize=16)
+ax.legend()
+fig.show()
diff --git a/docs/_downloads/b9f56e19f2500dad10214cc29a2a24e4/plot_mne_and_scikit_estimators.ipynb b/docs/_downloads/b9f56e19f2500dad10214cc29a2a24e4/plot_mne_and_scikit_estimators.ipynb
new file mode 100644
index 00000000..2fff5778
--- /dev/null
+++ b/docs/_downloads/b9f56e19f2500dad10214cc29a2a24e4/plot_mne_and_scikit_estimators.ipynb
@@ -0,0 +1,216 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# MNE Epochs-based pipelines\n\nThis example shows how to use machine learning pipeline based on MNE Epochs\ninstead of Numpy arrays. This is useful to make the most of the MNE code base\nand to embed EEG specific code inside sklearn pipelines.\n\nWe will compare different pipelines for P300:\n- Logistic regression, based on MNE Epochs\n- XDAWN and Logistic Regression (LR), based on MNE Epochs\n- XDAWN extended covariance and LR on tangent space, based on Numpy\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Sylvain Chevallier\n#\n# License: BSD (3-clause)\n# sphinx_gallery_thumbnail_number = 2\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom mne.decoding import Vectorizer\nfrom mne.preprocessing import Xdawn\nfrom pyriemann.estimation import XdawnCovariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nimport moabb\nfrom moabb.analysis.meta_analysis import (  # noqa: E501\n    compute_dataset_statistics,\n    find_significant_differences,\n)\nfrom moabb.analysis.plotting import paired_plot, summary_plot\nfrom moabb.datasets import BNCI2014_009\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import P300\n\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading Dataset\n\nLoad 2 subjects of BNCI 2014-009 dataset, with 3 session each\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = BNCI2014_009()\ndataset.subject_list = dataset.subject_list[:3]\ndatasets = [dataset]\nparadigm = P300()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get Data (optional)\n\nTo get access to the EEG signals downloaded from the dataset, you could\nuse ``dataset.get_data([subject_id)`` to obtain the EEG as MNE Epochs, stored\nin a dictionary of sessions and runs.\nThe ``paradigm.get_data(dataset=dataset, subjects=[subject_id])`` allows to\nobtain the preprocessed EEG data, the labels and the meta information. By\ndefault, the EEG is return as a Numpy array. With ``return_epochs=True``, MNE\nEpochs are returned.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subject_list = [1]\nsessions = dataset.get_data(subject_list)\nX, labels, meta = paradigm.get_data(dataset=dataset, subjects=subject_list)\nepochs, labels, meta = paradigm.get_data(\n    dataset=dataset, subjects=subject_list, return_epochs=True\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## A Simple MNE Pipeline\n\nUsing ``return_epochs=True`` in the evaluation, it is possible to design a\npipeline based on MNE Epochs input. Let's create a simple one, that\nreshape the input data from epochs, rescale the data and uses a logistic\nregression to classify the data. We will need to write a basic Transformer\nestimator, that complies with\n[sklearn convention](https://scikit-learn.org/stable/developers/develop.html).\nThis transformer will extract the data from an input Epoch, and reshapes into\n2D array.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "class MyVectorizer(BaseEstimator, TransformerMixin):\n    def __init__(self):\n        pass\n\n    def fit(self, X, y=None):\n        arr = X.get_data()\n        self.features_shape_ = arr.shape[1:]\n        return self\n\n    def transform(self, X, y=None):\n        arr = X.get_data()\n        return arr.reshape(len(arr), -1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will define a pipeline that is based on this new class, using a scaler\nand a logistic regression. This pipeline is evaluated across session using\nROC-AUC metric.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mne_ppl = {}\nmne_ppl[\"MNE LR\"] = make_pipeline(\n    MyVectorizer(), StandardScaler(), LogisticRegression(penalty=\"l1\", solver=\"liblinear\")\n)\n\nmne_eval = CrossSessionEvaluation(\n    paradigm=paradigm,\n    datasets=datasets,\n    suffix=\"examples\",\n    overwrite=True,\n    return_epochs=True,\n)\nmne_res = mne_eval.process(mne_ppl)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Advanced MNE Pipeline\n\nIn some case, the MNE pipeline should have access to the original labels from\nthe dataset. This is the case for the XDAWN code of MNE. One could pass\n`mne_labels` to evaluation in order to keep this label.\nAs an example, we will define a pipeline that computes an XDAWN filter, rescale,\nthen apply a logistic regression.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mne_adv = {}\nmne_adv[\"XDAWN LR\"] = make_pipeline(\n    Xdawn(n_components=5, reg=\"ledoit_wolf\", correct_overlap=False),\n    Vectorizer(),\n    StandardScaler(),\n    LogisticRegression(penalty=\"l1\", solver=\"liblinear\"),\n)\nadv_eval = CrossSessionEvaluation(\n    paradigm=paradigm,\n    datasets=datasets,\n    suffix=\"examples\",\n    overwrite=True,\n    return_epochs=True,\n    mne_labels=True,\n)\nadv_res = mne_eval.process(mne_adv)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Numpy-based Pipeline\n\nFor the comparison, we will define a Numpy-based pipeline that relies on\npyriemann to estimate XDAWN-extended covariance matrices that are projected\non the tangent space and classified with a logistic regression.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sk_ppl = {}\nsk_ppl[\"RG LR\"] = make_pipeline(\n    XdawnCovariances(nfilter=5, estimator=\"lwf\", xdawn_estimator=\"scm\"),\n    TangentSpace(),\n    LogisticRegression(penalty=\"l1\", solver=\"liblinear\"),\n)\nsk_eval = CrossSessionEvaluation(\n    paradigm=paradigm,\n    datasets=datasets,\n    suffix=\"examples\",\n    overwrite=True,\n)\nsk_res = sk_eval.process(sk_ppl)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Combining Results\n\nEven if the results have been obtained by different evaluation processes, it\nis possible to combine the resulting DataFrames to analyze and plot the\nresults.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "all_res = pd.concat([mne_res, adv_res, sk_res])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We could compare the Euclidean and Riemannian performance using a `paired_plot`\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paired_plot(all_res, \"XDAWN LR\", \"RG LR\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "All the results could be compared and statistical analysis could highlight the\ndifferences between pipelines.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "stats = compute_dataset_statistics(all_res)\nP, T = find_significant_differences(stats)\nsummary_plot(P, T)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/c0d5130414c37580257e7e0b7de777e2/plot_grid_search_withinsession.ipynb b/docs/_downloads/c0d5130414c37580257e7e0b7de777e2/plot_grid_search_withinsession.ipynb
new file mode 100644
index 00000000..13e489c7
--- /dev/null
+++ b/docs/_downloads/c0d5130414c37580257e7e0b7de777e2/plot_grid_search_withinsession.ipynb
@@ -0,0 +1,144 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# GridSearch within a session\n\nThis example demonstrates how to make a model selection in pipelines\nfor finding the best model parameter, using grid search. Two models\nare compared, one \"vanilla\" model with model tuned via grid search.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\nfrom pickle import load\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import Pipeline\n\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import MotorImagery\n\n\n# Initialize parameter for the Band Pass filter\nfmin = 8\nfmax = 35\ntmax = None\n\n# Select the Subject\nsubjects = [1]\n# Load the dataset\ndataset = BNCI2014_001()\n\nevents = [\"right_hand\", \"left_hand\"]\n\nparadigm = MotorImagery(\n    events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmax=tmax\n)\n\n# Create a path and folder for every subject\npath = os.path.join(str(\"Results\"))\nos.makedirs(path, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create the Pipelines\nTwo pipelines implementing elastic net classifiers, one using a fixed\nl1_ratio (\"VanillaEN\") and the other using a range of values to select\nl1_ratio (\"GridSearchEN\")\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\npipelines[\"VanillaEN\"] = Pipeline(\n    steps=[\n        (\"Covariances\", Covariances(\"cov\")),\n        (\"Tangent_Space\", TangentSpace(metric=\"riemann\")),\n        (\n            \"LogistReg\",\n            LogisticRegression(\n                penalty=\"elasticnet\",\n                l1_ratio=0.75,\n                intercept_scaling=1000.0,\n                solver=\"saga\",\n                max_iter=1000,\n            ),\n        ),\n    ]\n)\n\npipelines[\"GridSearchEN\"] = Pipeline(\n    steps=[\n        (\"Covariances\", Covariances(\"cov\")),\n        (\"Tangent_Space\", TangentSpace(metric=\"riemann\")),\n        (\n            \"LogistReg\",\n            LogisticRegression(\n                penalty=\"elasticnet\",\n                l1_ratio=0.70,\n                intercept_scaling=1000.0,\n                solver=\"saga\",\n                max_iter=1000,\n            ),\n        ),\n    ]\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The search space for parameters is defined as a dictionary, specifying the\nname of the estimator and the parameter name as a key.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "param_grid = {}\nparam_grid[\"GridSearchEN\"] = {\n    \"LogistReg__l1_ratio\": [0.15, 0.30, 0.45, 0.60, 0.75],\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Running the Evaluation\nIf a param_grid is specified during process, the specified pipelines will\nautomatically be run with a grid search.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset.subject_list = dataset.subject_list[:1]\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm,\n    datasets=dataset,\n    overwrite=True,\n    random_state=42,\n    hdf5_path=path,\n    n_jobs=-1,\n    save_model=True,\n)\nresult = evaluation.process(pipelines, param_grid)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\nThe grid search allows to find better parameter during the\nevaluation, leading to better accuracy results.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axes = plt.subplots(1, 1, figsize=[8, 5], sharey=True)\n\nsns.stripplot(\n    data=result,\n    y=\"score\",\n    x=\"pipeline\",\n    ax=axes,\n    jitter=True,\n    alpha=0.5,\n    zorder=1,\n    palette=\"Set1\",\n)\nsns.pointplot(data=result, y=\"score\", x=\"pipeline\", ax=axes, palette=\"Set1\")\naxes.set_ylabel(\"ROC AUC\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load Best Model Parameter\nThe best model are automatically saved in a pickle file, in the\nresults directory. It is possible to load those model for each\ndataset, subject and session. Here, we could see that the grid\nsearch found a l1_ratio that is different from the baseline\nvalue.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "with open(\n    \"./Results/Models_WithinSession/BNCI2014-001/1/1test/GridSearchEN/fitted_model_best.pkl\",\n    \"rb\",\n) as pickle_file:\n    GridSearchEN_Session_E = load(pickle_file)\n\nprint(\n    \"Best Parameter l1_ratio Session_E GridSearchEN \",\n    GridSearchEN_Session_E.best_params_[\"LogistReg__l1_ratio\"],\n)\n\nprint(\n    \"Best Parameter l1_ratio Session_E VanillaEN: \",\n    pipelines[\"VanillaEN\"].steps[2][1].l1_ratio,\n)\n\nwith open(\n    \"./Results/Models_WithinSession/BNCI2014-001/1/0train/GridSearchEN/fitted_model_best.pkl\",\n    \"rb\",\n) as pickle_file:\n    GridSearchEN_Session_T = load(pickle_file)\n\nprint(\n    \"Best Parameter l1_ratio Session_T GridSearchEN \",\n    GridSearchEN_Session_T.best_params_[\"LogistReg__l1_ratio\"],\n)\n\nprint(\n    \"Best Parameter l1_ratio Session_T VanillaEN: \",\n    pipelines[\"VanillaEN\"].steps[2][1].l1_ratio,\n)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/c0e8dffad0e66be84b9a762a9f24a516/plot_explore_paradigm.ipynb b/docs/_downloads/c0e8dffad0e66be84b9a762a9f24a516/plot_explore_paradigm.ipynb
new file mode 100644
index 00000000..9c89c869
--- /dev/null
+++ b/docs/_downloads/c0e8dffad0e66be84b9a762a9f24a516/plot_explore_paradigm.ipynb
@@ -0,0 +1,288 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Explore Paradigm Object\n\nA paradigm defines how the raw data will be converted to trials ready\nto be processed by a decoding algorithm. This is a function of the paradigm\nused, i.e. in motor imagery one can have two-class, multi-class,\nor continuous paradigms; similarly, different preprocessing is necessary\nfor ERP vs ERD paradigms.\n\nA paradigm also defines the appropriate evaluation metric, for example AUC\nfor binary classification problems, accuracy for multiclass, or kappa\ncoefficients for continuous paradigms.\n\nThis tutorial explores the paradigm object, with 3 examples of paradigm :\n     - MotorImagery\n     - FilterBankMotorImagery\n     - LeftRightImagery\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>\n#          Sylvain Chevallier <sylvain.chevallier@uvsq.fr>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.paradigms import FilterBankMotorImagery, LeftRightImagery, MotorImagery\n\n\nprint(__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## MotorImagery\n\nFirst, let's take an example of the MotorImagery paradigm.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = MotorImagery(n_classes=4)\n\nprint(paradigm.__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The function `get_data` allow you to access preprocessed data from a dataset.\nthis function will return 3 objects. A numpy array containing the\npreprocessed EEG data, the labels, and a dataframe with metadata.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(paradigm.get_data.__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Lets take the example of the BNCI2014_001 dataset, known as the dataset IIa\nfrom the BCI competition IV. We will load the data from the subject 1.\nWhen calling `get_data`, the paradigm will retrieve the data from the\nspecified list of subjects, apply preprocessing (by default, a bandpass\nbetween 7 and 35 Hz), epoch the data (with interval specified by the dataset,\nunless superseded by the paradigm) and return the corresponding objects.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = BNCI2014_001()\nsubjects = [1]\n\nX, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The epoched data is a 3D array, with epochs on the first dimension (here\n576 trials), channels on the second (22 channels) and time sample on the last\none.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(X.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Labels contains the labels corresponding to each trial. in the case of this\ndataset, we have the 4 types of motor imagery that was performed.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(np.unique(y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Metadata have at least 3 columns: subject, session and run.\n\n- subject is the subject id of the corresponding trial\n- session is the session id. A session denotes a recording made without\n  removing the EEG cap.\n- run is the individual continuous recording made during a session. A session\n  may or may not contain multiple runs.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(metadata.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For this data, we have one subject, 2 sessions (2 different recording days)\nand 6 runs per session.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(metadata.describe(include=\"all\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Paradigm objects can also return the list of all dataset compatible. Here\nit will return the list all the imagery datasets from the MOABB.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "compatible_datasets = paradigm.datasets\nprint([dataset.code for dataset in compatible_datasets])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## FilterBank MotorImagery\n\nFilterBankMotorImagery is the same paradigm, but with a different\npreprocessing. In this case, it applies a bank of 6 bandpass filter on the data\nbefore concatenating the output.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = FilterBankMotorImagery()\n\nprint(paradigm.__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Therefore, the output X is a 4D array, with trial x channel x time x filter\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)\n\nprint(X.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## LeftRight MotorImagery\n\nLeftRightImagery is a variation over the BaseMotorImagery paradigm,\nrestricted to left- and right-hand events.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\n\nprint(paradigm.__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The compatible dataset list is a subset of motor imagery dataset that\ncontains at least left and right hand events.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "compatible_datasets = paradigm.datasets\nprint([dataset.code for dataset in compatible_datasets])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "So if we apply this to our original dataset, it will only return trials\ncorresponding to left- and right-hand motor imagination.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)\n\nprint(np.unique(y))"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/c231a2e535b6091b8fee66bcebfa041c/changing_download_directory.ipynb b/docs/_downloads/c231a2e535b6091b8fee66bcebfa041c/changing_download_directory.ipynb
new file mode 100644
index 00000000..7dc1fffc
--- /dev/null
+++ b/docs/_downloads/c231a2e535b6091b8fee66bcebfa041c/changing_download_directory.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Change Download Directory\n\nThis is a minimal example to demonstrate how to change the default data\ndownload directory to a custom path/location. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Divyesh Narayanan <divyesh.narayanan@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport os.path as osp\n\nfrom mne import get_config\n\nfrom moabb.utils import set_download_dir"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can choose to change the download directory to any path of your choice.\nIf the path/folder doesn't exist, it will be created for you.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "original_path = get_config(\"MNE_DATA\")\nprint(f\"The download directory is currently {original_path}\")\nnew_path = osp.join(osp.expanduser(\"~\"), \"mne_data_test\")\nset_download_dir(new_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You could verify that the MNE config has been changed correctly\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "check_path = get_config(\"MNE_DATA\")\nprint(f\"Now the download directory has been changed to {check_path}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set the directory back to default location\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "set_download_dir(original_path)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/c82f3928cb70181ccb714f9ce973e8e7/plot_Getting_Started.ipynb b/docs/_downloads/c82f3928cb70181ccb714f9ce973e8e7/plot_Getting_Started.ipynb
new file mode 100644
index 00000000..8f9f5f8e
--- /dev/null
+++ b/docs/_downloads/c82f3928cb70181ccb714f9ce973e8e7/plot_Getting_Started.ipynb
@@ -0,0 +1,234 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Tutorial 0: Getting Started\n\nThis tutorial takes you through a basic working example of how to use this\ncodebase, including all the different components, up to the results\ngeneration. If you'd like to know about the statistics and plotting, see the\nnext tutorial.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Vinay Jayaram <vinayjayaram13@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\nTo use the codebase you need an evaluation and a paradigm, some algorithms,\nand a list of datasets to run it all on. You can find those in the following\nsubmodules; detailed tutorials are given for each of them.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import SVC"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you would like to specify the logging level when it is running, you can\nuse the standard python logging commands through the top-level moabb module\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import moabb\nfrom moabb.datasets import BNCI2014_001, utils\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\nfrom moabb.pipelines.features import LogVariance"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In order to create pipelines within a script, you will likely need at least\nthe make_pipeline function. They can also be specified via a .yml file. Here\nwe will make a couple pipelines just for convenience\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "moabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create pipelines\n\nWe create two pipelines: channel-wise log variance followed by LDA, and\nchannel-wise log variance followed by a cross-validated SVM (note that a\ncross-validation via scikit-learn cannot be described in a .yml file). For\nlater in the process, the pipelines need to be in a dictionary where the key\nis the name of the pipeline and the value is the Pipeline object\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\npipelines[\"AM+LDA\"] = make_pipeline(LogVariance(), LDA())\nparameters = {\"C\": np.logspace(-2, 2, 10)}\nclf = GridSearchCV(SVC(kernel=\"linear\"), parameters)\npipe = make_pipeline(LogVariance(), clf)\n\npipelines[\"AM+SVM\"] = pipe"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Datasets\n\nDatasets can be specified in many ways: Each paradigm has a property\n'datasets' which returns the datasets that are appropriate for that paradigm\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(LeftRightImagery().datasets)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Or you can run a search through the available datasets:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(utils.dataset_search(paradigm=\"imagery\", min_subjects=6))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Or you can simply make your own list (which we do here due to computational\nconstraints)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:2]\ndatasets = [dataset]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Paradigm\n\nParadigms define the events, epoch time, bandpass, and other preprocessing\nparameters. They have defaults that you can read in the documentation, or you\ncan simply set them as we do here. A single paradigm defines a method for\ngoing from continuous data to trial data of a fixed size. To learn more look\nat the tutorial Exploring Paradigms\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fmin = 8\nfmax = 35\nparadigm = LeftRightImagery(fmin=fmin, fmax=fmax)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nAn evaluation defines how the training and test sets are chosen. This could\nbe cross-validated within a single recording, or across days, or sessions, or\nsubjects. This also is the correct place to specify multiple threads.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "evaluation = CrossSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"examples\", overwrite=False\n)\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Results are returned as a pandas DataFrame, and from here you can do as you\nwant with them\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(results.head())"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py b/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py
new file mode 100644
index 00000000..2bea99d1
--- /dev/null
+++ b/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py
@@ -0,0 +1,152 @@
+"""
+===============================================================
+Cross-session motor imagery with deep learning EEGNet v4 model
+===============================================================
+This example shows how to use BrainDecode in combination with MOABB evaluation.
+In this example, we use the architecture EEGNetv4.
+"""
+
+# Authors: Igor Carrara <igor.carrara@inria.fr>
+#          Bruno Aristimunha <b.aristimunha@gmail.com>
+#
+# License: BSD (3-clause)
+
+import matplotlib.pyplot as plt
+import mne
+import seaborn as sns
+import torch
+from braindecode import EEGClassifier
+from braindecode.models import EEGNetv4
+from sklearn.pipeline import make_pipeline
+from skorch.callbacks import EarlyStopping, EpochScoring
+from skorch.dataset import ValidSplit
+
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import MotorImagery
+from moabb.utils import setup_seed
+
+
+mne.set_log_level(False)
+
+# Print Information PyTorch
+print(f"Torch Version: {torch.__version__}")
+
+# Set up GPU if it is there
+cuda = torch.cuda.is_available()
+device = "cuda" if cuda else "cpu"
+print("GPU is", "AVAILABLE" if cuda else "NOT AVAILABLE")
+
+###############################################################################
+# In this example, we will use only the dataset ``BNCI2014_001``.
+#
+# Running the benchmark
+# ---------------------
+#
+# This example uses the CrossSession evaluation procedure. We focus on the dataset BNCI2014_001 and only on 1 subject
+# to reduce computational time.
+#
+# To keep the computational time low, the epoch is reduced. In a real situation, we suggest using the following:
+# EPOCH = 1000
+# PATIENCE = 300
+#
+# This code is implemented to run on the CPU. If you're using a GPU, do not use multithreading
+# (i.e. set n_jobs=1)
+
+
+# Set random seed to be able to reproduce results
+seed = 42
+setup_seed(seed)
+
+# Ensure that all operations are deterministic on GPU (if used) for reproducibility
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
+# Hyperparameter
+LEARNING_RATE = 0.0625 * 0.01  # parameter taken from Braindecode
+WEIGHT_DECAY = 0  # parameter taken from Braindecode
+BATCH_SIZE = 64  # parameter taken from BrainDecode
+EPOCH = 10
+PATIENCE = 3
+fmin = 4
+fmax = 100
+tmin = 0
+tmax = None
+
+# Load the dataset
+dataset = BNCI2014_001()
+events = ["right_hand", "left_hand"]
+paradigm = MotorImagery(
+    events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax
+)
+subjects = [1]
+X, _, _ = paradigm.get_data(dataset=dataset, subjects=subjects)
+
+##############################################################################
+# Create Pipelines
+# ----------------
+# In order to create a pipeline, we need to load a model from braindecode.
+# the second step is to define a skorch model using EEGClassifier from braindecode
+# that allows converting the PyTorch model in a scikit-learn classifier.
+# Here, we will use the EEGNet v4 model [1]_ .
+# This model has mandatory hyperparameters (the number of channels, the number of classes,
+# and the temporal length of the input) but we do not need to specify them because they will
+# be set dynamically by EEGClassifier using the input data during the call to the ``.fit()`` method.
+
+# Define a Skorch classifier
+clf = EEGClassifier(
+    module=EEGNetv4,
+    optimizer=torch.optim.Adam,
+    optimizer__lr=LEARNING_RATE,
+    batch_size=BATCH_SIZE,
+    max_epochs=EPOCH,
+    train_split=ValidSplit(0.2, random_state=seed),
+    device=device,
+    callbacks=[
+        EarlyStopping(monitor="valid_loss", patience=PATIENCE),
+        EpochScoring(
+            scoring="accuracy", on_train=True, name="train_acc", lower_is_better=False
+        ),
+        EpochScoring(
+            scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False
+        ),
+    ],
+    verbose=1,  # Not printing the results for each epoch
+)
+
+# Create the pipelines
+pipes = {}
+pipes["EEGNetV4"] = make_pipeline(clf)
+
+##############################################################################
+# Evaluation
+# ----------
+dataset.subject_list = dataset.subject_list[:2]
+
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm,
+    datasets=dataset,
+    suffix="braindecode_example",
+    overwrite=True,
+    return_epochs=True,
+    n_jobs=1,
+)
+
+results = evaluation.process(pipes)
+
+print(results.head())
+
+##############################################################################
+# Plot Results
+# ----------------
+plt.figure()
+sns.barplot(data=results, y="score", x="subject", palette="viridis")
+plt.show()
+##############################################################################
+# References
+# ----------
+# .. [1] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,
+#    Hung, C. P., & Lance, B. J. (2018). `EEGNet: a compact convolutional neural
+#    network for EEG-based brain-computer interfaces.
+#    <https://doi.org/10.1088/1741-2552/aace8c>`_
+#    Journal of neural engineering, 15(5), 056013.
diff --git a/docs/_downloads/cab7a090c4183ca69dc0cd84d3b04413/auto_tutorials_python.zip b/docs/_downloads/cab7a090c4183ca69dc0cd84d3b04413/auto_tutorials_python.zip
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diff --git a/docs/_downloads/cd325bfe64e15ce23db3b200e83ba9a2/plot_filterbank_csp_vs_csp.ipynb b/docs/_downloads/cd325bfe64e15ce23db3b200e83ba9a2/plot_filterbank_csp_vs_csp.ipynb
new file mode 100644
index 00000000..f020e97a
--- /dev/null
+++ b/docs/_downloads/cd325bfe64e15ce23db3b200e83ba9a2/plot_filterbank_csp_vs_csp.ipynb
@@ -0,0 +1,126 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# FilterBank CSP versus CSP\n\nThis example show a comparison of CSP versus FilterBank CSP on the\nvery popular dataset 2a from the BCI competition IV.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import FilterBankLeftRightImagery, LeftRightImagery\nfrom moabb.pipelines.utils import FilterBank\n\n\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nThe CSP implementation from MNE is used. We selected 8 CSP components, as\nusually done in the literature.\n\nThe second pipeline is the filter bank CSP. We use the FilterBank object\nwith a CSP estimator. We set up the CSP to 4 components, to compensate for\nthe higher dimensionality.\n\nThe two pipelines will be applied on two different paradigm, so they have\ntheir own dict.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\npipelines[\"CSP+LDA\"] = make_pipeline(CSP(n_components=8), LDA())\n\npipelines_fb = {}\npipelines_fb[\"FBCSP+LDA\"] = make_pipeline(FilterBank(CSP(n_components=4)), LDA())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nSince two different preprocessing will be applied, we have two different\nparadigm objects. We have to make sure their filter matches so the comparison\nwill be fair.\n\nThe first one is a standard `LeftRightImagery` with a 8 to 35 Hz broadband\nfilter.\n\nThe second is a `FilterBankLeftRightImagery` with a bank of 2 filters, ranging\nfrom 8 to 35 Hz.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Because this is being auto-generated we only use 2 subjects\ndataset = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:2]\ndatasets = [dataset]\noverwrite = False  # set to True if we want to overwrite cached results\n\n# broadband filters\nfmin = 8\nfmax = 35\nparadigm = LeftRightImagery(fmin=fmin, fmax=fmax)\nevaluation = CrossSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"examples\", overwrite=overwrite\n)\nresults = evaluation.process(pipelines)\n\n# Bank of 2 filters\nfilters = [[8, 24], [16, 32]]\nparadigm = FilterBankLeftRightImagery(filters=filters)\nevaluation = CrossSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"examples\", overwrite=overwrite\n)\nresults_fb = evaluation.process(pipelines_fb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After processing the two, we simply concatenate the results.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results = pd.concat([results, results_fb])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we plot the results via seaborn. We first display a pointplot\nwith the average performance of each pipeline across session and subjects.\nThe second plot is a paired scatter plot. Each point representing the score\nof a single session. An algorithm will outperform another is most of the\npoints are in its quadrant.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=[8, 4], sharey=True)\n\nsns.stripplot(\n    data=results,\n    y=\"score\",\n    x=\"pipeline\",\n    ax=axes[0],\n    jitter=True,\n    alpha=0.5,\n    zorder=1,\n    palette=\"Set1\",\n)\nsns.pointplot(data=results, y=\"score\", x=\"pipeline\", ax=axes[0], palette=\"Set1\")\n\naxes[0].set_ylabel(\"ROC AUC\")\naxes[0].set_ylim(0.5, 1)\n\n# paired plot\npaired = results.pivot_table(\n    values=\"score\", columns=\"pipeline\", index=[\"subject\", \"session\"]\n)\npaired = paired.reset_index()\n\nsns.regplot(data=paired, y=\"FBCSP+LDA\", x=\"CSP+LDA\", ax=axes[1], fit_reg=False)\naxes[1].plot([0, 1], [0, 1], ls=\"--\", c=\"k\")\naxes[1].set_xlim(0.5, 1)\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/de620aa43eea2fc28a9b3587adf0d1f8/plot_disk_cache.py b/docs/_downloads/de620aa43eea2fc28a9b3587adf0d1f8/plot_disk_cache.py
new file mode 100644
index 00000000..ed0e9e92
--- /dev/null
+++ b/docs/_downloads/de620aa43eea2fc28a9b3587adf0d1f8/plot_disk_cache.py
@@ -0,0 +1,264 @@
+"""
+=================================================
+Cache on disk intermediate data processing states
+=================================================
+
+This example shows how intermediate data processing
+states can be cached on disk to speed up the loading
+of this data in subsequent calls.
+
+When a MOABB paradigm processes a dataset, it will
+first apply processing steps to the raw data, this is
+called the ``raw_pipeline``. Then, it will convert the
+raw data into epochs and apply processing steps on the
+epochs, this is called the ``epochs_pipeline``.
+Finally, it will eventually convert the epochs into arrays,
+this is called the ``array_pipeline``. In summary:
+
+``raw_pipeline`` --> ``epochs_pipeline`` --> ``array_pipeline``
+
+After each step, MOABB offers the possibility to save on disk
+the result of the step. This is done by setting the ``cache_config``
+parameter of the paradigm's ``get_data`` method.
+The ``cache_config`` parameter is a dictionary that can take all
+the parameters of ``moabb.datasets.base.CacheConfig`` as keys,
+they are the following: ``use``, ``save_raw``, ``save_epochs``,
+``save_array``, ``overwrite_raw``, ``overwrite_epochs``,
+``overwrite_array``, and ``path``.  You can also directly pass a
+``CacheConfig`` object as ``cache_config``.
+
+If ``use=False``, the ``save_*`` and ``overwrite_*``
+parameters are ignored.
+
+When trying to use the cache (i.e. ``use=True``), MOABB will
+first check if there exist a cache of the result of the full
+pipeline (i.e. ``raw_pipeline`` --> ``epochs_pipeline`` ->
+``array_pipeline``).
+If there is none, we remove the last step of the pipeline and
+look for its cached result. We keep removing steps and looking
+for a cached result until we find one or until we reach an
+empty pipeline.
+Every time, if the ``overwrite_*`` parameter
+of the corresponding step is true, we first try to erase the
+cache of this step.
+Once a cache has been found or the empty pipeline has been reached,
+depending on the case we either load the cache or the original dataset.
+Then, apply the missing steps one by one and save their result
+if their corresponding ``save_*`` parameter is true.
+
+By default, only the result of the ``raw_pipeline`` is saved.
+This is usually a good compromise between speed and disk space
+because, when using cached raw data, the epochs can be obtained
+without preloading the whole raw signals, only the necessary
+intervals. Yet, because only the raw data is cached, the epoching
+parameters can be changed without creating a new cache each time.
+However, if your epoching parameters are fixed, you can directly
+cache the epochs or the arrays to speed up the loading and
+reduce the disk space used.
+
+.. note::
+    The ``cache_config`` parameter is also available for the ``get_data``
+    method of the datasets. It works the same way as for a
+    paradigm except that it will save un-processed raw recordings.
+"""
+
+# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>
+#
+# License: BSD (3-clause)
+
+import shutil
+import tempfile
+
+###############################################################################
+import time
+from pathlib import Path
+
+from moabb import set_log_level
+from moabb.datasets import Zhou2016
+from moabb.paradigms import LeftRightImagery
+
+
+set_log_level("info")
+
+###############################################################################
+# Basic usage
+# -----------
+#
+# The ``cache_config`` parameter is a dictionary that has the
+# following default values:
+default_cache_config = dict(
+    save_raw=False,
+    save_epochs=False,
+    save_array=False,
+    use=False,
+    overwrite_raw=False,
+    overwrite_epochs=False,
+    overwrite_array=False,
+    path=None,
+)
+
+###############################################################################
+# You don not need to specify all the keys of ``cache_config``, only the ones
+# you want to change.
+#
+# By default, the cache is saved at the MNE data directory (i.e. when
+# ``path=None``).  The MNE data directory can be found with
+# ``mne.get_config('MNE_DATA')``. For this example, we will save it  in a
+# temporary directory instead:
+temp_dir = Path(tempfile.mkdtemp())
+
+###############################################################################
+# We will use the Zhou2016 dataset and the LeftRightImagery paradigm in this
+# example, but this works for any dataset and paradigm pair.:
+dataset = Zhou2016()
+paradigm = LeftRightImagery()
+
+###############################################################################
+# And we will only use the first subject for this example:
+subjects = [1]
+
+###############################################################################
+# Then, saving a cache can simply be done by setting the desired parameters
+# in the ``cache_config`` dictionary:
+cache_config = dict(
+    use=True,
+    save_raw=True,
+    save_epochs=True,
+    save_array=True,
+    path=temp_dir,
+)
+_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)
+
+###############################################################################
+# Time comparison
+# ---------------
+#
+# Now, we will compare the time it takes to load the with different levels of
+# cache. For this, we will use the cache saved in the previous block and
+# overwrite the steps results one by one so that we can compare the time it
+# takes to load the data and compute the missing steps with an increasing
+# number of missing steps.
+#
+# Using array cache:
+cache_config = dict(
+    use=True,
+    path=temp_dir,
+    save_raw=False,
+    save_epochs=False,
+    save_array=False,
+    overwrite_raw=False,
+    overwrite_epochs=False,
+    overwrite_array=False,
+)
+t0 = time.time()
+_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)
+t_array = time.time() - t0
+
+###############################################################################
+# Using epochs cache:
+cache_config = dict(
+    use=True,
+    path=temp_dir,
+    save_raw=False,
+    save_epochs=False,
+    save_array=False,
+    overwrite_raw=False,
+    overwrite_epochs=False,
+    overwrite_array=True,
+)
+t0 = time.time()
+_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)
+t_epochs = time.time() - t0
+
+###############################################################################
+# Using raw cache:
+cache_config = dict(
+    use=True,
+    path=temp_dir,
+    save_raw=False,
+    save_epochs=False,
+    save_array=False,
+    overwrite_raw=False,
+    overwrite_epochs=True,
+    overwrite_array=True,
+)
+t0 = time.time()
+_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)
+t_raw = time.time() - t0
+
+###############################################################################
+# Using no cache:
+cache_config = dict(
+    use=False,
+    path=temp_dir,
+    save_raw=False,
+    save_epochs=False,
+    save_array=False,
+    overwrite_raw=False,
+    overwrite_epochs=False,
+    overwrite_array=False,
+)
+t0 = time.time()
+_ = paradigm.get_data(dataset, subjects, cache_config=cache_config)
+t_nocache = time.time() - t0
+
+###############################################################################
+# Time needed to load the data with different levels of cache:
+print(f"Using array cache: {t_array:.2f} seconds")
+print(f"Using epochs cache: {t_epochs:.2f} seconds")
+print(f"Using raw cache: {t_raw:.2f} seconds")
+print(f"Without cache: {t_nocache:.2f} seconds")
+
+###############################################################################
+# As you can see, using a raw cache is more than 5 times faster than
+# without cache.
+# This is because when using the raw cache, the data is not preloaded, only
+# the desired epochs are loaded in memory.
+#
+# Using the epochs cache is a little faster than the raw cache. This is because
+# there are several preprocessing steps done after the epoching by the
+# ``epochs_pipeline``. This difference would be greater if the ``resample``
+# argument was different that the sampling frequency of the dataset. Indeed,
+# the data loading time is directly proportional to its sampling frequency
+# and the resampling is done by the ``epochs_pipeline``.
+#
+# Finally, we observe very little difference between array and epochs cache.
+# The main interest of the array cache is when the user passes a
+# computationally heavy but fixed additional preprocessing (for example
+# computing the covariance matrices of the epochs). This can be done by using
+# the ``postprocess_pipeline`` argument. The output of this additional pipeline
+# (necessary a numpy array) will be saved to avoid re-computing it each time.
+#
+#
+# Technical details
+# -----------------
+#
+# Under the hood, the cache is saved on disk in a Brain Imaging Data Structure
+# (BIDS) compliant format. More details on this structure can be found in the
+# tutorial :doc:`./plot_bids_conversion`.
+#
+# However, there are two particular aspects of the way MOABB saves the data
+# that are not specific to BIDS:
+#
+# * For each file, we set a
+#   `description key <https://bids-specification.readthedocs.io/en/stable/appendices/entities.html#desc>`_.
+#   This key is a code that corresponds to a hash of the
+#   pipeline that was used to generate the data (i.e. from raw to the state
+#   of the cache). This code is unique for each different pipeline and allows
+#   to identify all the files that were generated by the same pipeline.
+# * Once we finish saving all the files for a given combination of dataset,
+#   subject, and pipeline, we write a file ending in ``"_lockfile.json"`` at
+#   the root directory of this subject. This file serves two purposes:
+#
+#   * It indicates that the cache is complete for this subject and pipeline.
+#     If it is not present, it means that something went wrong during the
+#     saving process and the cache is incomplete.
+#   * The file contains the un-hashed string representation of the pipeline.
+#     Therefore, it can be used to identify the pipeline used without having
+#     to decode the description key.
+#
+# Cleanup
+# -------
+#
+# Finally, we can delete the temporary folder:
+shutil.rmtree(temp_dir)
diff --git a/docs/_downloads/e34acbbaad3fb8832beb7fa8e4557c94/plot_learning_curve_p300_external.ipynb b/docs/_downloads/e34acbbaad3fb8832beb7fa8e4557c94/plot_learning_curve_p300_external.ipynb
new file mode 100644
index 00000000..b32284fa
--- /dev/null
+++ b/docs/_downloads/e34acbbaad3fb8832beb7fa8e4557c94/plot_learning_curve_p300_external.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Within Session P300 with Learning Curve\n\nThis example shows how to perform a within session analysis while also\ncreating learning curves for a P300 dataset.\nAdditionally, we will evaluate external code. Make sure to have tdlda installed\n, which can be found in requirements_external.txt\n\nWe will compare three pipelines :\n\n- Riemannian geometry\n- Jumping Means-based Linear Discriminant Analysis\n- Time-Decoupled Linear Discriminant Analysis\n\nWe will use the P300 paradigm, which uses the AUC as metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Jan Sosulski\n#\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom pyriemann.estimation import XdawnCovariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.pipeline import make_pipeline\nfrom tdlda import TimeDecoupledLda\nfrom tdlda import Vectorizer as JumpingMeansVectorizer\n\nimport moabb\nfrom moabb.datasets import BNCI2014_009\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import P300\n\n\n# getting rid of the warnings about the future (on s'en fout !)\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\n\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "processing_sampling_rate = 128\npipelines = {}\n\n# We have to do this because the classes are called 'Target' and 'NonTarget'\n# but the evaluation function uses a LabelEncoder, transforming them\n# to 0 and 1\nlabels_dict = {\"Target\": 1, \"NonTarget\": 0}\n\n# Riemannian geometry based classification\npipelines[\"RG+LDA\"] = make_pipeline(\n    XdawnCovariances(nfilter=5, estimator=\"lwf\", xdawn_estimator=\"scm\"),\n    TangentSpace(),\n    LDA(solver=\"lsqr\", shrinkage=\"auto\"),\n)\n\n# Simple LDA pipeline using averaged feature values in certain time intervals\njumping_mean_ivals = [\n    [0.10, 0.139],\n    [0.14, 0.169],\n    [0.17, 0.199],\n    [0.20, 0.229],\n    [0.23, 0.269],\n    [0.27, 0.299],\n    [0.30, 0.349],\n    [0.35, 0.409],\n    [0.41, 0.449],\n    [0.45, 0.499],\n]\njmv = JumpingMeansVectorizer(\n    fs=processing_sampling_rate, jumping_mean_ivals=jumping_mean_ivals\n)\n\npipelines[\"JM+LDA\"] = make_pipeline(jmv, LDA(solver=\"lsqr\", shrinkage=\"auto\"))\n\n# Time-decoupled Covariance classifier, needs information about number of\n# channels and time intervals\nc = TimeDecoupledLda(N_channels=16, N_times=10)\n# TD-LDA needs to know about the used jumping means intervals\nc.preproc = jmv\npipelines[\"JM+TD-LDA\"] = make_pipeline(jmv, c)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nWe define the paradigm (P300) and use the BNCI 2014-009 dataset for it.\nThe evaluation will return a dataframe containing AUCs for each permutation\nand dataset size.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = P300(resample=processing_sampling_rate)\ndataset = BNCI2014_009()\n# Remove the slicing of the subject list to evaluate multiple subjects\ndataset.subject_list = dataset.subject_list[0:1]\ndatasets = [dataset]\noverwrite = True  # set to True if we want to overwrite cached results\ndata_size = dict(policy=\"ratio\", value=np.geomspace(0.02, 1, 6))\n# When the training data is sparse, perform more permutations than when we have\n# a lot of data\nn_perms = np.floor(np.geomspace(20, 2, len(data_size[\"value\"]))).astype(int)\nprint(n_perms)\n# Guarantee reproducibility\nnp.random.seed(7536298)\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm,\n    datasets=datasets,\n    data_size=data_size,\n    n_perms=n_perms,\n    suffix=\"examples_lr\",\n    overwrite=overwrite,\n)\n\nresults = evaluation.process(pipelines)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we plot the results.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(facecolor=\"white\", figsize=[8, 4])\n\nn_subs = len(dataset.subject_list)\n\nif n_subs > 1:\n    r = results.groupby([\"pipeline\", \"subject\", \"data_size\"]).mean().reset_index()\nelse:\n    r = results\n\nsns.pointplot(data=r, x=\"data_size\", y=\"score\", hue=\"pipeline\", ax=ax, palette=\"Set1\")\n\nerrbar_meaning = \"subjects\" if n_subs > 1 else \"permutations\"\ntitle_str = f\"Errorbar shows Mean-CI across {errbar_meaning}\"\nax.set_xlabel(\"Amount of training samples\")\nax.set_ylabel(\"ROC AUC\")\nax.set_title(title_str)\nfig.tight_layout()\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/f1e72fecd296ad91b914eb3562c881ee/plot_cross_session_motor_imagery.ipynb b/docs/_downloads/f1e72fecd296ad91b914eb3562c881ee/plot_cross_session_motor_imagery.ipynb
new file mode 100644
index 00000000..b12e056f
--- /dev/null
+++ b/docs/_downloads/f1e72fecd296ad91b914eb3562c881ee/plot_cross_session_motor_imagery.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cross-Session Motor Imagery\n\nThis example show how to perform a cross session motor imagery analysis on the\nvery popular dataset 2a from the BCI competition IV.\n\nWe will compare two pipelines :\n\n- CSP+LDA\n- Riemannian Geometry+Logistic Regression\n\nWe will use the LeftRightImagery paradigm. This will restrict the analysis\nto two classes (left hand versus right hand) and use AUC as metric.\n\nThe cross session evaluation context will evaluate performance using a leave\none session out cross-validation. For each session in the dataset, a model\nis trained on every other session and performance are evaluated on the current\nsession.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>\n#          Sylvain Chevallier <sylvain.chevallier@uvsq.fr>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom mne.decoding import CSP\nfrom pyriemann.estimation import Covariances\nfrom pyriemann.tangentspace import TangentSpace\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery\n\n\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nPipelines must be a dict of sklearn pipeline transformer.\n\nThe CSP implementation is based on the MNE implementation. We selected 8 CSP\ncomponents, as usually done in the literature.\n\nThe Riemannian geometry pipeline consists in covariance estimation, tangent\nspace mapping and finally a logistic regression for the classification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipelines = {}\n\npipelines[\"CSP+LDA\"] = make_pipeline(CSP(n_components=8), LDA())\n\npipelines[\"RG+LR\"] = make_pipeline(\n    Covariances(), TangentSpace(), LogisticRegression(solver=\"lbfgs\")\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nWe define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).\nThe evaluation will return a DataFrame containing a single AUC score for\neach subject / session of the dataset, and for each pipeline.\n\nResults are saved into the database, so that if you add a new pipeline, it\nwill not run again the evaluation unless a parameter has changed. Results can\nbe overwritten if necessary.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\n# Because this is being auto-generated we only use 2 subjects\ndataset = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:2]\ndatasets = [dataset]\noverwrite = False  # set to True if we want to overwrite cached results\nevaluation = CrossSessionEvaluation(\n    paradigm=paradigm, datasets=datasets, suffix=\"examples\", overwrite=overwrite\n)\n\nresults = evaluation.process(pipelines)\n\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we plot the results. We first make a pointplot with the average\nperformance of each pipeline across session and subjects.\nThe second plot is a paired scatter plot. Each point representing the score\nof a single session. An algorithm will outperform another is most of the\npoints are in its quadrant.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=[8, 4], sharey=True)\n\nsns.stripplot(\n    data=results,\n    y=\"score\",\n    x=\"pipeline\",\n    ax=axes[0],\n    jitter=True,\n    alpha=0.5,\n    zorder=1,\n    palette=\"Set1\",\n)\nsns.pointplot(data=results, y=\"score\", x=\"pipeline\", ax=axes[0], palette=\"Set1\")\n\naxes[0].set_ylabel(\"ROC AUC\")\naxes[0].set_ylim(0.5, 1)\n\npaired = results.pivot_table(\n    values=\"score\", columns=\"pipeline\", index=[\"subject\", \"session\"]\n)\npaired = paired.reset_index()\n\nsns.regplot(data=paired, y=\"RG+LR\", x=\"CSP+LDA\", ax=axes[1], fit_reg=False)\naxes[1].plot([0, 1], [0, 1], ls=\"--\", c=\"k\")\naxes[1].set_xlim(0.5, 1)\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/f2d00b929c914067772799fd6f99fb75/4_adding_a_dataset.ipynb b/docs/_downloads/f2d00b929c914067772799fd6f99fb75/4_adding_a_dataset.ipynb
new file mode 100644
index 00000000..e5a8bf7d
--- /dev/null
+++ b/docs/_downloads/f2d00b929c914067772799fd6f99fb75/4_adding_a_dataset.ipynb
@@ -0,0 +1,133 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Tutorial 4: Creating a dataset class\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier\n#\n# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019\n\nimport mne\nimport numpy as np\nfrom pyriemann.classification import MDM\nfrom pyriemann.estimation import Covariances\nfrom scipy.io import loadmat, savemat\nfrom sklearn.pipeline import make_pipeline\n\nfrom moabb.datasets import download as dl\nfrom moabb.datasets.base import BaseDataset\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import LeftRightImagery"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating some Data\n\nTo illustrate the creation of a dataset class in MOABB, we first create an\nexample dataset saved in .mat file. It contains a single fake recording on\n8 channels lasting for 150 seconds (sampling frequency 256 Hz). We have\nincluded the script that creates this dataset and have uploaded it online.\nThe fake dataset is available on the\n[Zenodo website](https://sandbox.zenodo.org/record/369543)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def create_example_dataset():\n    \"\"\"Create a fake example for a dataset.\"\"\"\n    sfreq = 256\n    t_recording = 150\n    t_trial = 1  # duration of a trial\n    intertrial = 2  # time between end of a trial and the next one\n    n_chan = 8\n\n    x = np.zeros((n_chan + 1, t_recording * sfreq))  # electrodes + stimulus\n    stim = np.zeros(t_recording * sfreq)\n    t_offset = 1.0  # offset where the trials start\n    n_trials = 40\n\n    rep = np.linspace(0, 4 * t_trial, t_trial * sfreq)\n    signal = np.sin(2 * np.pi / t_trial * rep)\n    for n in range(n_trials):\n        label = n % 2 + 1  # alternate between class 0 and class 1\n        tn = int(t_offset * sfreq + n * (t_trial + intertrial) * sfreq)\n        stim[tn] = label\n        noise = 0.1 * np.random.randn(n_chan, len(signal))\n        x[:-1, tn : (tn + t_trial * sfreq)] = label * signal + noise\n    x[-1, :] = stim\n    return x, sfreq\n\n\n# Create the fake data\nfor subject in [1, 2, 3]:\n    x, fs = create_example_dataset()\n    filename = \"subject_\" + str(subject).zfill(2) + \".mat\"\n    mdict = {}\n    mdict[\"x\"] = x\n    mdict[\"fs\"] = fs\n    savemat(filename, mdict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating a Dataset Class\n\nWe will create now a dataset class using the fake data simulated with the\ncode from above. For this, we first need to import the right classes from\nMOABB:\n\n- ``dl`` is a very useful script that downloads automatically a dataset online\n  if it is not yet available in the user's computer. The script knows where\n  to download the files because we create a global variable telling the URL\n  where to fetch the data.\n- ``BaseDataset`` is the basic class that we overload to create our dataset.\n\nThe global variable with the dataset's URL should specify an online\nrepository where all the files are stored.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ExampleDataset_URL = \"https://sandbox.zenodo.org/record/369543/files/\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The ``ExampleDataset`` needs to implement only 3 functions:\n\n- ``__init__`` for indicating the parameter of the dataset\n- ``_get_single_subject_data`` to define how to process the data once they\n  have been downloaded\n- ``data_path`` to define how the data are downloaded.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "class ExampleDataset(BaseDataset):\n    \"\"\"Dataset used to exemplify the creation of a dataset class in MOABB.\n\n    The data samples have been simulated and has no physiological\n    meaning whatsoever.\n    \"\"\"\n\n    def __init__(self):\n        super().__init__(\n            subjects=[1, 2, 3],\n            sessions_per_subject=1,\n            events={\"left_hand\": 1, \"right_hand\": 2},\n            code=\"ExampleDataset\",\n            interval=[0, 0.75],\n            paradigm=\"imagery\",\n            doi=\"\",\n        )\n\n    def _get_single_subject_data(self, subject):\n        \"\"\"Return data for a single subject.\"\"\"\n        file_path_list = self.data_path(subject)\n\n        data = loadmat(file_path_list[0])\n        x = data[\"x\"]\n        fs = data[\"fs\"]\n        ch_names = [\"ch\" + str(i) for i in range(8)] + [\"stim\"]\n        ch_types = [\"eeg\" for i in range(8)] + [\"stim\"]\n        info = mne.create_info(ch_names, fs, ch_types)\n        raw = mne.io.RawArray(x, info)\n\n        sessions = {}\n        sessions[\"0\"] = {}\n        sessions[\"0\"][\"0\"] = raw\n        return sessions\n\n    def data_path(\n        self, subject, path=None, force_update=False, update_path=None, verbose=None\n    ):\n        \"\"\"Download the data from one subject.\"\"\"\n        if subject not in self.subject_list:\n            raise (ValueError(\"Invalid subject number\"))\n\n        url = \"{:s}subject_0{:d}.mat\".format(ExampleDataset_URL, subject)\n        path = dl.data_dl(url, \"ExampleDataset\")\n        return [path]  # it has to return a list"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Using the ExampleDataset\n\nNow that the `ExampleDataset` is defined, it could be instantiated directly.\nThe rest of the code follows the steps described in the previous tutorials.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = ExampleDataset()\n\nparadigm = LeftRightImagery()\nX, labels, meta = paradigm.get_data(dataset=dataset, subjects=[1])\n\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=dataset, overwrite=False, suffix=\"newdataset\"\n)\npipelines = {}\npipelines[\"MDM\"] = make_pipeline(Covariances(\"oas\"), MDM(metric=\"riemann\"))\nscores = evaluation.process(pipelines)\n\nprint(scores)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Pushing on MOABB Github\n\nIf you want to make your dataset available to everyone, you could upload\nyour data on public server (like Zenodo or Figshare) and signal that you\nwant to add your dataset to MOABB in the  [dedicated issue](https://github.com/NeuroTechX/moabb/issues/1).  # noqa: E501\nYou could then follow the instructions on [how to contribute](https://github.com/NeuroTechX/moabb/blob/master/CONTRIBUTING.md)  # noqa: E501\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/f3215cecaa532ec823443b1b0630d3fb/plot_within_session_ssvep.ipynb b/docs/_downloads/f3215cecaa532ec823443b1b0630d3fb/plot_within_session_ssvep.ipynb
new file mode 100644
index 00000000..fdcbe75d
--- /dev/null
+++ b/docs/_downloads/f3215cecaa532ec823443b1b0630d3fb/plot_within_session_ssvep.ipynb
@@ -0,0 +1,180 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Within Session SSVEP\n\nThis Example show how to perform a within-session SSVEP analysis on the\nMAMEM dataset 3, using a CCA pipeline.\n\nThe within-session evaluation assesses the performance of a classification\npipeline using a 5-fold cross-validation. The reported metric (here, accuracy)\nis the average of all fold.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>\n#\n# License: BSD (3-clause)\n\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.pipeline import make_pipeline\n\nimport moabb\nfrom moabb.datasets import MAMEM3\nfrom moabb.evaluations import WithinSessionEvaluation\nfrom moabb.paradigms import SSVEP\nfrom moabb.pipelines import SSVEP_CCA\n\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nwarnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\nmoabb.set_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading Dataset\n\nLoad 2 subjects of MAMEM3 dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subj = [1, 3]\ndataset = MAMEM3()\ndataset.subject_list = subj"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Choose Paradigm\n\nWe select the paradigm SSVEP, applying a bandpass filter (3-15 Hz) on\nthe data and we keep only the first 3 classes, that is stimulation\nfrequency of 6.66, 7.50 and 8.57 Hz.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = SSVEP(fmin=3, fmax=15, n_classes=3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Pipelines\n\nUse a Canonical Correlation Analysis classifier\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "interval = dataset.interval\nfreqs = paradigm.used_events(dataset)\n\npipeline = {}\npipeline[\"CCA\"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=3))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get Data (optional)\n\nTo get access to the EEG signals downloaded from the dataset, you could\nuse `dataset.get_data(subjects=[subject_id])` to obtain the EEG under\nMNE format, stored in a dictionary of sessions and runs.\nOtherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])`\nallows to obtain the EEG data in scikit format, the labels and the meta\ninformation. In `paradigm.get_data`, the EEG are preprocessed according\nto the paradigm requirement.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# sessions = dataset.get_data(subjects=[3])\n# X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[3])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluation\n\nThe evaluation will return a DataFrame containing a single AUC score for\neach subject and pipeline.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "overwrite = True  # set to True if we want to overwrite cached results\n\nevaluation = WithinSessionEvaluation(\n    paradigm=paradigm, datasets=dataset, suffix=\"examples\", overwrite=overwrite\n)\nresults = evaluation.process(pipeline)\n\nprint(results.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\nHere we plot the results, indicating the score for each subject\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "plt.figure()\nsns.barplot(data=results, y=\"score\", x=\"session\", hue=\"subject\", palette=\"viridis\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And the computation time in seconds\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "plt.figure()\nax = sns.barplot(data=results, y=\"time\", x=\"session\", hue=\"subject\", palette=\"Reds\")\nax.set_ylabel(\"Time (s)\")\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/f4558bdb453cb198095be347c0f12fe0/example_codecarbon.ipynb b/docs/_downloads/f4558bdb453cb198095be347c0f12fe0/example_codecarbon.ipynb
new file mode 100644
index 00000000..466893ef
--- /dev/null
+++ b/docs/_downloads/f4558bdb453cb198095be347c0f12fe0/example_codecarbon.ipynb
@@ -0,0 +1,151 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmarking with MOABB showing the CO2 footprint\n\nThis example shows how to use MOABB to track the CO2 footprint\nusing [CodeCarbon library](https://codecarbon.io/)_.\nFor this example, we will use only one\ndataset to keep the computation time low, but this benchmark is designed\nto easily scale to many datasets. Due to limitation of online documentation\ngeneration, the results is computed on a local cluster but could be easily\nreplicated on your infrastructure.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Igor Carrara <igor.carrara@inria.fr>\n#          Bruno Aristimunha <b.aristimunha@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from moabb import benchmark, set_log_level\nfrom moabb.analysis.plotting import codecarbon_plot\nfrom moabb.datasets import BNCI2014_001, Zhou2016\nfrom moabb.paradigms import LeftRightImagery\n\n\nset_log_level(\"info\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading the pipelines\n\nTo run this example we use several pipelines, ML and DL (Keras) and also\npipelines that need an optimization of the hyperparameter.\nAll this different pipelines are stored in ``pipelines_codecarbon``\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Selecting the datasets (optional)\n\nIf you want to limit your benchmark on a subset of datasets, you can use the\n``include_datasets`` and ``exclude_datasets`` arguments. You will need either\nto provide the dataset's object, or a dataset's code. To get the list of\navailable dataset's code for a given paradigm, you can use the following\ncommand:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "paradigm = LeftRightImagery()\nfor d in paradigm.datasets:\n    print(d.code)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we will use only the last dataset, 'Zhou 2016', considering\nonly the first subject.\n\n## Running the benchmark\n\nThe benchmark is run using the ``benchmark`` function. You need to specify the\nfolder containing the pipelines to use, the kind of evaluation and the paradigm\nto use. By default, the benchmark will use all available datasets for all\nparadigms listed in the pipelines. You could restrict to specific evaluation\nand paradigm using the ``evaluations`` and ``paradigms`` arguments.\n\nTo save computation time, the results are cached. If you want to re-run the\nbenchmark, you can set the ``overwrite`` argument to ``True``.\n\nIt is possible to indicate the folder to cache the results and the one to\nsave the analysis & figures. By default, the results are saved in the\n``results`` folder, and the analysis & figures are saved in the ``benchmark``\nfolder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Zhou2016()\ndataset2 = BNCI2014_001()\ndataset.subject_list = dataset.subject_list[:1]\ndataset2.subject_list = dataset2.subject_list[:1]\ndatasets = [dataset, dataset2]\n\nresults = benchmark(\n    pipelines=\"./pipelines_codecarbon/\",\n    evaluations=[\"WithinSession\"],\n    paradigms=[\"LeftRightImagery\"],\n    include_datasets=datasets,\n    results=\"./results/\",\n    overwrite=False,\n    plot=False,\n    output=\"./benchmark/\",\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Benchmark prints a summary of the results. Detailed results are saved in a\npandas dataframe, and can be used to generate figures. The analysis & figures\nare saved in the ``benchmark`` folder.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results.head()\n\norder_list = [\n    \"CSP + SVM\",\n    \"Tangent Space LR\",\n    \"EN Grid\",\n    \"CSP + LDA Grid\",\n    \"Keras_EEGNet_8_2\",\n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting the results\nWe can plot the results using the ``codecarbon_plot`` function, generated\nbelow. This function takes the dataframe returned by the ``benchmark``\nfunction as input, and returns a pyplot figure.\nThe ``order_list`` argument is used to specify the order of the pipelines in\nthe plot.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "codecarbon_plot(results, order_list, country=\"(France)\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The result expected will be the following image, but varying depending on the\n machine and the country used to run the example.\n\n .. image:: ../images/example_codecarbon.png\n    :align: center\n    :alt: carbon_example\n\n##############################################################################\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/fa1ca889a5b6c6678d3af932651fcf7f/plot_cross_session_multiple_datasets.py b/docs/_downloads/fa1ca889a5b6c6678d3af932651fcf7f/plot_cross_session_multiple_datasets.py
new file mode 100644
index 00000000..bcf19929
--- /dev/null
+++ b/docs/_downloads/fa1ca889a5b6c6678d3af932651fcf7f/plot_cross_session_multiple_datasets.py
@@ -0,0 +1,118 @@
+"""
+==================================
+Cross-Session on Multiple Datasets
+==================================
+
+This example shows how to perform a cross-session analysis on two MI datasets
+using a CSP+LDA pipeline
+
+The cross session evaluation context will evaluate performance using a leave
+one session out cross-validation. For each session in the dataset, a model
+is trained on every other session and performance are evaluated on the current
+session.
+"""
+
+# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
+#
+# License: BSD (3-clause)
+
+import warnings
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from mne.decoding import CSP
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+import moabb
+from moabb.datasets import BNCI2014_001, Zhou2016
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+moabb.set_log_level("info")
+
+###############################################################################
+# Loading Dataset
+# ---------------
+#
+# Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 session each
+
+subj = [1, 2]
+datasets = [Zhou2016(), BNCI2014_001()]
+for d in datasets:
+    d.subject_list = subj
+
+###############################################################################
+# Choose Paradigm
+# ---------------
+#
+# We select the paradigm MI, applying a bandpass filter (8-35 Hz) on
+# the data and we will keep only left- and right-hand motor imagery
+
+paradigm = LeftRightImagery(fmin=8, fmax=35)
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Use the Common Spatial Patterns with 8 components and a Linear Discriminant
+# Analysis classifier.
+
+pipeline = {}
+pipeline["CSP+LDA"] = make_pipeline(CSP(n_components=8), LDA())
+
+##############################################################################
+# Get Data (optional)
+# -------------------
+#
+# To get access to the EEG signals downloaded from the dataset, you could
+# use `dataset.get_data(subjects=[subject_id])` to obtain the EEG under
+# an MNE format, stored in a dictionary of sessions and runs.
+# Otherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])`
+# allows to obtain the EEG data in sklearn format, the labels and the meta
+# information. The data are preprocessed according to the paradigm
+# requirements.
+
+# X_all, labels_all, meta_all = [], [], []
+# for d in datasets:
+#     # sessions = d.get_data(subjects=[2])
+#     X, labels, meta = paradigm.get_data(dataset=d, subjects=[2])
+#     X_all.append(X)
+#     labels_all.append(labels)
+#     meta_all.append(meta)
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# The evaluation will return a DataFrame containing a single AUC score for
+# each subject / session of the dataset, and for each pipeline.
+
+overwrite = True  # set to True if we want to overwrite cached results
+
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
+)
+results = evaluation.process(pipeline)
+
+print(results.head())
+
+##############################################################################
+# Plot Results
+# ----------------
+#
+# Here we plot the results, indicating the score for each session and subject
+
+sns.catplot(
+    data=results,
+    x="session",
+    y="score",
+    hue="subject",
+    col="dataset",
+    kind="bar",
+    palette="viridis",
+)
+plt.show()
diff --git a/docs/_downloads/fb557f4e0ac4b423b8911acc0958ae76/plot_fixed_interval_windows.py b/docs/_downloads/fb557f4e0ac4b423b8911acc0958ae76/plot_fixed_interval_windows.py
new file mode 100644
index 00000000..ba9b8fae
--- /dev/null
+++ b/docs/_downloads/fb557f4e0ac4b423b8911acc0958ae76/plot_fixed_interval_windows.py
@@ -0,0 +1,128 @@
+"""
+=================================
+Fixed interval windows processing
+=================================
+
+This example shows how to process a dataset using the
+:class:`moabb.paradigms.FixedIntervalWindowsProcessing` paradigm. This paradigm
+creates epochs at fixed intervals, ignoring the stim
+channel and events of the datasets. Therefore, it is
+compatible with all the datasets. Unfortunately,
+this paradigm is not compatible with the MOABB evaluation
+framework. However, it can be used to process datasets
+for unsupervised algorithms.
+
+In this example, we will use the Zhou2016 dataset because
+it is relatively small and can be downloaded quickly.
+"""
+
+# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>
+#
+# License: BSD (3-clause)
+
+###############################################################################
+import matplotlib.pyplot as plt
+import mne
+import numpy as np
+
+from moabb import set_log_level
+from moabb.datasets import Zhou2016
+from moabb.paradigms import FixedIntervalWindowsProcessing, MotorImagery
+
+
+set_log_level("info")
+
+###############################################################################
+# Process a dataset
+# -----------------
+#
+# To process a dataset with
+# :class:`moabb.paradigms.FixedIntervalWindowsProcessing` , you can use the
+# method as with every other paradigm. The only additional parameters are
+# ``length``, ``stride``, ``start_offset``, and ``stop_offset``. They are
+# all parametrised in seconds. ``length`` is the length of the epochs,
+# ``stride`` is the time between the onset of two consecutive epochs,
+# ``start_offset`` is the offset between each run start and their first
+# epoch, and ``stop_offset`` is the offset between each run start and their
+# last epoch. The default values are ``length=5``, ``stride=10``,
+# ``start_offset=0``, and ``stop_offset=None`` (i.e. end of the run).
+#
+# An example usage of :class:`moabb.paradigms.FixedIntervalWindowsProcessing`
+# with the :class:`moabb.datasets.Zhou2016` dataset:
+dataset = Zhou2016()
+processing = FixedIntervalWindowsProcessing(
+    # new parameters:
+    length=100,
+    stride=50,
+    start_offset=300,
+    stop_offset=900,  # we epoch 10 minutes per run, starting at 5 minutes (i.e. 300 seconds)
+    # parameters common with other paradigms:
+    resample=100,
+    fmin=7,
+    fmax=45,
+    baseline=None,
+    channels=None,
+)
+X, labels, metadata = processing.get_data(dataset=dataset, subjects=[1])
+
+###############################################################################
+# In this dataset, there are three sessions per subject and two runs per
+# session:
+for column in metadata.columns:
+    print(f"{column}s: {metadata[column].unique()}")
+
+###############################################################################
+# We expect to obtained ``(stop_offset - start_offset - length) / stride``;
+# i.e. :math:`(900-300-100)/50=10` epochs per run. Here we have 3*2=6 runs.
+# And indeed, we obtain
+# a total of :math:`6*10=60` epochs:
+print(f"Number of epochs: {len(X)}")
+
+###############################################################################
+# .. note::
+#     To apply a bank of bandpass filters, you can use the
+#     :class:`moabb.paradigms.FilterBankFixedIntervalWindowsProcessing`
+#     paradigm instead.
+#
+# Print the events
+# ---------------------------------
+#
+# We can print the position of the created epochs within the run next to
+# the original events of the dataset. For this, we will first instantiate
+# a :class:`moabb.paradigms.MotorImagery` paradigm to recover the original
+# events of the dataset:
+paradigm = MotorImagery(
+    resample=100,
+    fmin=7,
+    fmax=45,
+    baseline=None,
+    channels=None,
+)
+
+###############################################################################
+# Then, we can recover the events of both paradigms using the
+# ``_get_events_pipeline`` method:
+events_pipeline_dataset = paradigm._get_events_pipeline(dataset)
+events_pipeline_fixed = processing._get_events_pipeline(dataset)
+raw = dataset.get_data(subjects=[1])[1]["0"]["0"]
+events_dataset = events_pipeline_dataset.transform(raw)
+events_fixed = events_pipeline_fixed.transform(raw)
+events = np.concatenate([events_dataset, events_fixed])
+event_id = dict(**paradigm.used_events(dataset), **processing.used_events(dataset))
+
+###############################################################################
+# Finally, we can plot the events. The artificial events created by
+# :class:`moabb.paradigms.FixedIntervalWindowsProcessing` are named
+# ``"Windows"``:
+fig = mne.viz.plot_events(
+    events,
+    sfreq=raw.info["sfreq"],
+    event_id=event_id,
+)
+fig.subplots_adjust(right=0.7)
+plt.show()
+
+###############################################################################
+# We can see that the epochs were effectively created at a fixed interval
+# every 50 seconds between 300 and 900 seconds, and ignoring
+# the original events of the dataset.
diff --git a/docs/_downloads/fc103af82bd37a6a36f57f863e9fd4aa/plot_statistical_analysis.py b/docs/_downloads/fc103af82bd37a6a36f57f863e9fd4aa/plot_statistical_analysis.py
new file mode 100644
index 00000000..4b41d6b8
--- /dev/null
+++ b/docs/_downloads/fc103af82bd37a6a36f57f863e9fd4aa/plot_statistical_analysis.py
@@ -0,0 +1,138 @@
+"""
+=======================
+Statistical Analysis
+=======================
+
+The MOABB codebase comes with convenience plotting utilities and some
+statistical testing. This tutorial focuses on what those exactly are and how
+they can be used.
+
+"""
+
+# Authors: Vinay Jayaram <vinayjayaram13@gmail.com>
+#
+# License: BSD (3-clause)
+# sphinx_gallery_thumbnail_number = -2
+
+import matplotlib.pyplot as plt
+from mne.decoding import CSP
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+
+import moabb
+import moabb.analysis.plotting as moabb_plt
+from moabb.analysis.meta_analysis import (  # noqa: E501
+    compute_dataset_statistics,
+    find_significant_differences,
+)
+from moabb.datasets import BNCI2014_001
+from moabb.evaluations import CrossSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+moabb.set_log_level("info")
+
+print(__doc__)
+
+###############################################################################
+# Results Generation
+# ---------------------
+#
+# First we need to set up a paradigm, dataset list, and some pipelines to
+# test. This is explored more in the examples -- we choose left vs right
+# imagery paradigm with a single bandpass. There is only one dataset here but
+# any number can be added without changing this workflow.
+#
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+#
+# The CSP implementation from MNE is used. We selected 8 CSP components, as
+# usually done in the literature.
+#
+# The Riemannian geometry pipeline consists in covariance estimation, tangent
+# space mapping and finally a logistic regression for the classification.
+
+pipelines = {}
+
+pipelines["CSP+LDA"] = make_pipeline(CSP(n_components=8), LDA())
+
+pipelines["RG+LR"] = make_pipeline(Covariances(), TangentSpace(), LogisticRegression())
+
+pipelines["CSP+LR"] = make_pipeline(CSP(n_components=8), LogisticRegression())
+
+pipelines["RG+LDA"] = make_pipeline(Covariances(), TangentSpace(), LDA())
+
+##############################################################################
+# Evaluation
+# ----------
+#
+# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
+# The evaluation will return a DataFrame containing a single AUC score for
+# each subject / session of the dataset, and for each pipeline.
+#
+# Results are saved into the database, so that if you add a new pipeline, it
+# will not run again the evaluation unless a parameter has changed. Results can
+# be overwritten if necessary.
+
+paradigm = LeftRightImagery()
+dataset = BNCI2014_001()
+dataset.subject_list = dataset.subject_list[:4]
+datasets = [dataset]
+overwrite = True  # set to False if we want to use cached results
+evaluation = CrossSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="stats", overwrite=overwrite
+)
+
+results = evaluation.process(pipelines)
+
+##############################################################################
+# MOABB Plotting
+# ----------------
+#
+# Here we plot the results using some of the convenience methods within the
+# toolkit.  The score_plot visualizes all the data with one score per subject
+# for every dataset and pipeline.
+
+fig = moabb_plt.score_plot(results)
+plt.show()
+
+###############################################################################
+# For a comparison of two algorithms, there is the paired_plot, which plots
+# performance in one versus the performance in the other over all chosen
+# datasets. Note that there is only one score per subject, regardless of the
+# number of sessions.
+
+fig = moabb_plt.paired_plot(results, "CSP+LDA", "RG+LDA")
+plt.show()
+
+###############################################################################
+# Statistical Testing and Further Plots
+# ----------------------------------------
+#
+# If the statistical significance of results is of interest, the method
+# compute_dataset_statistics allows one to show a meta-analysis style plot as
+# well. For an overview of how all algorithms perform in comparison with each
+# other, the method find_significant_differences and the summary_plot are
+# possible.
+
+stats = compute_dataset_statistics(results)
+P, T = find_significant_differences(stats)
+
+###############################################################################
+# The meta-analysis style plot shows the standardized mean difference within
+# each tested dataset for the two algorithms in question, in addition to a
+# meta-effect and significance both per-dataset and overall.
+fig = moabb_plt.meta_analysis_plot(stats, "CSP+LDA", "RG+LDA")
+plt.show()
+
+###############################################################################
+# The summary plot shows the effect and significance related to the hypothesis
+# that the algorithm on the y-axis significantly outperformed the algorithm on
+# the x-axis over all datasets
+moabb_plt.summary_plot(P, T)
+plt.show()
diff --git a/docs/_downloads/fe7c33d68ac9fa4d60c0efbc341f93f3/tutorial_3_benchmarking_multiple_pipelines.py b/docs/_downloads/fe7c33d68ac9fa4d60c0efbc341f93f3/tutorial_3_benchmarking_multiple_pipelines.py
new file mode 100644
index 00000000..dcb84ff4
--- /dev/null
+++ b/docs/_downloads/fe7c33d68ac9fa4d60c0efbc341f93f3/tutorial_3_benchmarking_multiple_pipelines.py
@@ -0,0 +1,93 @@
+"""
+===========================================
+Tutorial 3: Benchmarking multiple pipelines
+===========================================
+
+In this last part, we extend the previous example by assessing the
+classification score of not one but three classification pipelines.
+"""
+
+# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
+#
+# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
+
+import warnings
+
+import matplotlib.pyplot as plt
+import mne
+import seaborn as sns
+from mne.decoding import CSP
+from pyriemann.classification import MDM
+from pyriemann.estimation import Covariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import SVC
+
+import moabb
+from moabb.datasets import BNCI2014_001, Zhou2016
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import LeftRightImagery
+
+
+mne.set_log_level("CRITICAL")
+moabb.set_log_level("info")
+warnings.filterwarnings("ignore")
+
+##############################################################################
+# Creating Pipelines
+# ------------------
+#
+# We instantiate the three different classiciation pipelines to be considered
+# in the analysis. The object that gathers each pipeline is a dictionary. The
+# first pipeline is the CSP+LDA that we have seen in the previous parts. The
+# other two pipelines rely on Riemannian geometry, using an SVM classification
+# in the tangent space of the covariance matrices estimated from the EEG or a
+# MDM classifier that works directly on covariance matrices.
+
+pipelines = {}
+pipelines["csp+lda"] = make_pipeline(CSP(n_components=8), LDA())
+pipelines["tgsp+svm"] = make_pipeline(
+    Covariances("oas"), TangentSpace(metric="riemann"), SVC(kernel="linear")
+)
+pipelines["MDM"] = make_pipeline(Covariances("oas"), MDM(metric="riemann"))
+
+##############################################################################
+# The following lines go exactly as in the previous tutorial, where we end up
+# obtaining a pandas dataframe containing the results of the evaluation.
+datasets = [BNCI2014_001(), Zhou2016()]
+subj = [1, 2, 3]
+for d in datasets:
+    d.subject_list = subj
+paradigm = LeftRightImagery()
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, overwrite=False
+)
+results = evaluation.process(pipelines)
+
+##############################################################################
+# As `overwrite` is set to False, the results from the previous tutorial are reused and
+# only the new pipelines are evaluated. The results from "csp+lda" are not recomputed.
+# The results are saved in ~/mne_data/results if the parameter `hdf5_path` is not set.
+
+##############################################################################
+# Plotting Results
+# ----------------
+#
+# The following plot shows a comparison of the three classification pipelines
+# for each subject of each dataset.
+
+results["subj"] = [str(resi).zfill(2) for resi in results["subject"]]
+g = sns.catplot(
+    kind="bar",
+    x="score",
+    y="subj",
+    hue="pipeline",
+    col="dataset",
+    height=12,
+    aspect=0.5,
+    data=results,
+    orient="h",
+    palette="viridis",
+)
+plt.show()
diff --git a/docs/_images/architecture.png b/docs/_images/architecture.png
new file mode 100644
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diff --git a/docs/_images/sphx_glr_plot_explore_paradigm_thumb.png b/docs/_images/sphx_glr_plot_explore_paradigm_thumb.png
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diff --git a/docs/_images/sphx_glr_plot_within_session_ssvep_thumb.png b/docs/_images/sphx_glr_plot_within_session_ssvep_thumb.png
new file mode 100644
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diff --git a/docs/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/docs/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
new file mode 100644
index 00000000..3225661c
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+++ b/docs/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
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diff --git a/docs/_static/_sphinx_javascript_frameworks_compat.js b/docs/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 00000000..8549469d
--- /dev/null
+++ b/docs/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,134 @@
+/*
+ * _sphinx_javascript_frameworks_compat.js
+ * ~~~~~~~~~~
+ *
+ * Compatability shim for jQuery and underscores.js.
+ *
+ * WILL BE REMOVED IN Sphinx 6.0
+ * xref RemovedInSphinx60Warning
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
new file mode 100644
index 00000000..18495ea0
--- /dev/null
+++ b/docs/_static/basic.css
@@ -0,0 +1,900 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 270px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
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+    margin-bottom: 10px;
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+}
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+}
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+}
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+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
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+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
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+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
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+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
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+    margin: 1em 0;
+}
+
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+}
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+
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+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
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+    font-weight: bold;
+}
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+}
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+    font-size: 1.2em;
+}
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+    background-color: transparent;
+}
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+    font-size: 1.3em;
+}
+
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+    font-size: larger;
+}
+
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+	font-style: italic;
+}
+
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+
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+	font-family: unset;
+}
+
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+	color: #0033B3;
+}
+
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+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
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+}
+
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+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+    clear: both;
+}
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+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
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+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
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+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
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+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
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+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
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+}
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+    font-weight: bold;
+    font-size: 1.1em;
+}
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+    font-style: italic;
+}
+
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+    padding: 5px;
+    border: 3px solid red;
+}
+
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+    background-color: #ffa;
+}
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+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
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+}
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+}
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+}
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+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
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+}
+
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+}
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+}
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+    padding: 0;
+}
+
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+}
+
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+    overflow: hidden;
+}
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+}
+
+div.highlight pre,
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+}
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+div.code-block-caption + div {
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+}
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+    padding: 2px 5px;
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+}
+
+div.code-block-caption code {
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+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
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+    font-style: italic;
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+    font-weight: bold;
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+
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+}
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+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
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+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
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new file mode 100644
index 00000000..92fad4b5
--- /dev/null
+++ b/docs/_static/check-solid.svg
@@ -0,0 +1,4 @@
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+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/docs/_static/clipboard.min.js b/docs/_static/clipboard.min.js
new file mode 100644
index 00000000..54b3c463
--- /dev/null
+++ b/docs/_static/clipboard.min.js
@@ -0,0 +1,7 @@
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+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
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e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/docs/_static/copy-button.svg b/docs/_static/copy-button.svg
new file mode 100644
index 00000000..b888a20d
--- /dev/null
+++ b/docs/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-clipboard" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#2c3e50" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M9 5h-2a2 2 0 0 0 -2 2v12a2 2 0 0 0 2 2h10a2 2 0 0 0 2 -2v-12a2 2 0 0 0 -2 -2h-2" />
+  <rect x="9" y="3" width="6" height="4" rx="2" />
+</svg>
diff --git a/docs/_static/copybutton.css b/docs/_static/copybutton.css
new file mode 100644
index 00000000..5d291490
--- /dev/null
+++ b/docs/_static/copybutton.css
@@ -0,0 +1,81 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .5em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    border: #e1e1e1 1px solid;
+    background-color: rgb(245, 245, 245);
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+}
+
+button.copybtn img {
+    width: 100%;
+    padding: .2em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+.highlight:hover button.copybtn {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
diff --git a/docs/_static/copybutton.js b/docs/_static/copybutton.js
new file mode 100644
index 00000000..482bda03
--- /dev/null
+++ b/docs/_static/copybutton.js
@@ -0,0 +1,197 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copié dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+const path_static = `${doc_url_root}_static/`;
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for two seconds, then changes it back
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
+  setTimeout(() => el.classList.remove('success'), 2000)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  img = el.querySelector("img");
+  img.setAttribute('src', `${path_static}check-solid.svg`)
+  setTimeout(() => img.setAttribute('src', `${path_static}copy-button.svg`), 2000)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const codeCells = document.querySelectorAll('div.highlight pre')
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      <img src="${path_static}copy-button.svg" alt="${messages[locale]['copy_to_clipboard']}">
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+  return formatCopyText(target.innerText, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/docs/_static/copybutton_funcs.js b/docs/_static/copybutton_funcs.js
new file mode 100644
index 00000000..b9168c55
--- /dev/null
+++ b/docs/_static/copybutton_funcs.js
@@ -0,0 +1,58 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css
new file mode 100644
index 00000000..dc9503b5
--- /dev/null
+++ b/docs/_static/css/custom.css
@@ -0,0 +1,10 @@
+.large-text {
+    font-size: 30px;
+}
+
+.bd-main .bd-content .bd-article-container {
+  max-width: 75em;  /* default is 60em */
+}
+.bd-page-width {
+  max-width: 88em;  /* default is 88rem */
+}
diff --git a/docs/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/docs/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
new file mode 100644
index 00000000..3225661c
--- /dev/null
+++ b/docs/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
@@ -0,0 +1 @@
+.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}a.sd-bg-success:focus,a.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}.sd-bg-info{background-color:var(--sd-color-info) !important}.sd-bg-text-info{color:var(--sd-color-info-text) !important}button.sd-bg-info:focus,button.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}a.sd-bg-info:focus,a.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}.sd-bg-warning{background-color:var(--sd-color-warning) !important}.sd-bg-text-warning{color:var(--sd-color-warning-text) !important}button.sd-bg-warning:focus,button.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}a.sd-bg-warning:focus,a.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}.sd-bg-danger{background-color:var(--sd-color-danger) !important}.sd-bg-text-danger{color:var(--sd-color-danger-text) !important}button.sd-bg-danger:focus,button.sd-bg-danger:hover{background-color:var(--sd-color-danger-highlight) !important}a.sd-bg-danger:focus,a.sd-bg-danger:hover{background-color:var(--sd-color-danger-highlight) !important}.sd-bg-light{background-color:var(--sd-color-light) !important}.sd-bg-text-light{color:var(--sd-color-light-text) !important}button.sd-bg-light:focus,button.sd-bg-light:hover{background-color:var(--sd-color-light-highlight) !important}a.sd-bg-light:focus,a.sd-bg-light:hover{background-color:var(--sd-color-light-highlight) !important}.sd-bg-muted{background-color:var(--sd-color-muted) !important}.sd-bg-text-muted{color:var(--sd-color-muted-text) !important}button.sd-bg-muted:focus,button.sd-bg-muted:hover{background-color:var(--sd-color-muted-highlight) !important}a.sd-bg-muted:focus,a.sd-bg-muted:hover{background-color:var(--sd-color-muted-highlight) !important}.sd-bg-dark{background-color:var(--sd-color-dark) 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auto;width:50%}.sd-col-lg-7{-ms-flex:0 0 auto;flex:0 0 auto;width:58.3333333333%}.sd-col-lg-8{-ms-flex:0 0 auto;flex:0 0 auto;width:66.6666666667%}.sd-col-lg-9{-ms-flex:0 0 auto;flex:0 0 auto;width:75%}.sd-col-lg-10{-ms-flex:0 0 auto;flex:0 0 auto;width:83.3333333333%}.sd-col-lg-11{-ms-flex:0 0 auto;flex:0 0 auto;width:91.6666666667%}.sd-col-lg-12{-ms-flex:0 0 auto;flex:0 0 auto;width:100%}.sd-g-lg-0,.sd-gy-lg-0{--sd-gutter-y: 0}.sd-g-lg-0,.sd-gx-lg-0{--sd-gutter-x: 0}.sd-g-lg-1,.sd-gy-lg-1{--sd-gutter-y: 0.25rem}.sd-g-lg-1,.sd-gx-lg-1{--sd-gutter-x: 0.25rem}.sd-g-lg-2,.sd-gy-lg-2{--sd-gutter-y: 0.5rem}.sd-g-lg-2,.sd-gx-lg-2{--sd-gutter-x: 0.5rem}.sd-g-lg-3,.sd-gy-lg-3{--sd-gutter-y: 1rem}.sd-g-lg-3,.sd-gx-lg-3{--sd-gutter-x: 1rem}.sd-g-lg-4,.sd-gy-lg-4{--sd-gutter-y: 1.5rem}.sd-g-lg-4,.sd-gx-lg-4{--sd-gutter-x: 1.5rem}.sd-g-lg-5,.sd-gy-lg-5{--sd-gutter-y: 3rem}.sd-g-lg-5,.sd-gx-lg-5{--sd-gutter-x: 3rem}}@media(min-width: 1200px){.sd-col-xl-auto{-ms-flex:0 0 auto;flex:0 0 auto;width:auto}.sd-col-xl-1{-ms-flex:0 0 auto;flex:0 0 auto;width:8.3333333333%}.sd-col-xl-2{-ms-flex:0 0 auto;flex:0 0 auto;width:16.6666666667%}.sd-col-xl-3{-ms-flex:0 0 auto;flex:0 0 auto;width:25%}.sd-col-xl-4{-ms-flex:0 0 auto;flex:0 0 auto;width:33.3333333333%}.sd-col-xl-5{-ms-flex:0 0 auto;flex:0 0 auto;width:41.6666666667%}.sd-col-xl-6{-ms-flex:0 0 auto;flex:0 0 auto;width:50%}.sd-col-xl-7{-ms-flex:0 0 auto;flex:0 0 auto;width:58.3333333333%}.sd-col-xl-8{-ms-flex:0 0 auto;flex:0 0 auto;width:66.6666666667%}.sd-col-xl-9{-ms-flex:0 0 auto;flex:0 0 auto;width:75%}.sd-col-xl-10{-ms-flex:0 0 auto;flex:0 0 auto;width:83.3333333333%}.sd-col-xl-11{-ms-flex:0 0 auto;flex:0 0 auto;width:91.6666666667%}.sd-col-xl-12{-ms-flex:0 0 auto;flex:0 0 auto;width:100%}.sd-g-xl-0,.sd-gy-xl-0{--sd-gutter-y: 0}.sd-g-xl-0,.sd-gx-xl-0{--sd-gutter-x: 0}.sd-g-xl-1,.sd-gy-xl-1{--sd-gutter-y: 0.25rem}.sd-g-xl-1,.sd-gx-xl-1{--sd-gutter-x: 0.25rem}.sd-g-xl-2,.sd-gy-xl-2{--sd-gutter-y: 0.5rem}.sd-g-xl-2,.sd-gx-xl-2{--sd-gutter-x: 0.5rem}.sd-g-xl-3,.sd-gy-xl-3{--sd-gutter-y: 1rem}.sd-g-xl-3,.sd-gx-xl-3{--sd-gutter-x: 1rem}.sd-g-xl-4,.sd-gy-xl-4{--sd-gutter-y: 1.5rem}.sd-g-xl-4,.sd-gx-xl-4{--sd-gutter-x: 1.5rem}.sd-g-xl-5,.sd-gy-xl-5{--sd-gutter-y: 3rem}.sd-g-xl-5,.sd-gx-xl-5{--sd-gutter-x: 3rem}}.sd-flex-row-reverse{flex-direction:row-reverse !important}details.sd-dropdown{position:relative}details.sd-dropdown .sd-summary-title{font-weight:700;padding-right:3em !important;-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;user-select:none}details.sd-dropdown:hover{cursor:pointer}details.sd-dropdown .sd-summary-content{cursor:default}details.sd-dropdown summary{list-style:none;padding:1em}details.sd-dropdown summary .sd-octicon.no-title{vertical-align:middle}details.sd-dropdown[open] summary .sd-octicon.no-title{visibility:hidden}details.sd-dropdown summary::-webkit-details-marker{display:none}details.sd-dropdown summary:focus{outline:none}details.sd-dropdown .sd-summary-icon{margin-right:.5em}details.sd-dropdown .sd-summary-icon svg{opacity:.8}details.sd-dropdown summary:hover .sd-summary-up svg,details.sd-dropdown summary:hover .sd-summary-down svg{opacity:1;transform:scale(1.1)}details.sd-dropdown .sd-summary-up svg,details.sd-dropdown .sd-summary-down svg{display:block;opacity:.6}details.sd-dropdown .sd-summary-up,details.sd-dropdown .sd-summary-down{pointer-events:none;position:absolute;right:1em;top:1em}details.sd-dropdown[open]>.sd-summary-title .sd-summary-down{visibility:hidden}details.sd-dropdown:not([open])>.sd-summary-title .sd-summary-up{visibility:hidden}details.sd-dropdown:not([open]).sd-card{border:none}details.sd-dropdown:not([open])>.sd-card-header{border:1px solid var(--sd-color-card-border);border-radius:.25rem}details.sd-dropdown.sd-fade-in[open] summary~*{-moz-animation:sd-fade-in .5s ease-in-out;-webkit-animation:sd-fade-in .5s ease-in-out;animation:sd-fade-in .5s ease-in-out}details.sd-dropdown.sd-fade-in-slide-down[open] summary~*{-moz-animation:sd-fade-in .5s ease-in-out,sd-slide-down .5s ease-in-out;-webkit-animation:sd-fade-in .5s ease-in-out,sd-slide-down .5s ease-in-out;animation:sd-fade-in .5s ease-in-out,sd-slide-down .5s ease-in-out}.sd-col>.sd-dropdown{width:100%}.sd-summary-content>.sd-tab-set:first-child{margin-top:0}@keyframes sd-fade-in{0%{opacity:0}100%{opacity:1}}@keyframes sd-slide-down{0%{transform:translate(0, -10px)}100%{transform:translate(0, 0)}}.sd-tab-set{border-radius:.125rem;display:flex;flex-wrap:wrap;margin:1em 0;position:relative}.sd-tab-set>input{opacity:0;position:absolute}.sd-tab-set>input:checked+label{border-color:var(--sd-color-tabs-underline-active);color:var(--sd-color-tabs-label-active)}.sd-tab-set>input:checked+label+.sd-tab-content{display:block}.sd-tab-set>input:not(:checked)+label:hover{color:var(--sd-color-tabs-label-hover);border-color:var(--sd-color-tabs-underline-hover)}.sd-tab-set>input:focus+label{outline-style:auto}.sd-tab-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.sd-tab-set>label{border-bottom:.125rem solid transparent;margin-bottom:0;color:var(--sd-color-tabs-label-inactive);border-color:var(--sd-color-tabs-underline-inactive);cursor:pointer;font-size:var(--sd-fontsize-tabs-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .sd-tab-set>label:hover{color:var(--sd-color-tabs-label-active)}.sd-col>.sd-tab-set{width:100%}.sd-tab-content{box-shadow:0 -0.0625rem var(--sd-color-tabs-overline),0 .0625rem var(--sd-color-tabs-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.sd-tab-content>:first-child{margin-top:0 !important}.sd-tab-content>:last-child{margin-bottom:0 !important}.sd-tab-content>.sd-tab-set{margin:0}.sd-sphinx-override,.sd-sphinx-override *{-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box}.sd-sphinx-override p{margin-top:0}:root{--sd-color-primary: #007bff;--sd-color-secondary: #6c757d;--sd-color-success: #28a745;--sd-color-info: #17a2b8;--sd-color-warning: #f0b37e;--sd-color-danger: #dc3545;--sd-color-light: #f8f9fa;--sd-color-muted: #6c757d;--sd-color-dark: #212529;--sd-color-black: black;--sd-color-white: white;--sd-color-primary-highlight: #0069d9;--sd-color-secondary-highlight: #5c636a;--sd-color-success-highlight: #228e3b;--sd-color-info-highlight: #148a9c;--sd-color-warning-highlight: #cc986b;--sd-color-danger-highlight: #bb2d3b;--sd-color-light-highlight: #d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem}
diff --git a/docs/_static/design-tabs.js b/docs/_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/docs/_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
new file mode 100644
index 00000000..527b876c
--- /dev/null
+++ b/docs/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
new file mode 100644
index 00000000..fece0822
--- /dev/null
+++ b/docs/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: '1.0.0-dev',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: false,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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diff --git a/docs/_static/jquery-3.6.0.js b/docs/_static/jquery-3.6.0.js
new file mode 100644
index 00000000..fc6c299b
--- /dev/null
+++ b/docs/_static/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_static/jquery.js b/docs/_static/jquery.js
new file mode 100644
index 00000000..c4c6022f
--- /dev/null
+++ b/docs/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
new file mode 100644
index 00000000..2e22b06a
--- /dev/null
+++ b/docs/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
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+html[data-theme="light"] .highlight .gr { color: #E40000 } /* Generic.Error */
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+html[data-theme="light"] .highlight .gi { color: #008400 } /* Generic.Inserted */
+html[data-theme="light"] .highlight .go { color: #717171 } /* Generic.Output */
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+html[data-theme="light"] .highlight .gt { color: #0044DD } /* Generic.Traceback */
+html[data-theme="light"] .highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+html[data-theme="light"] .highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+html[data-theme="light"] .highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+html[data-theme="light"] .highlight .kp { color: #008000 } /* Keyword.Pseudo */
+html[data-theme="light"] .highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+html[data-theme="light"] .highlight .kt { color: #B00040 } /* Keyword.Type */
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+html[data-theme="light"] .highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+html[data-theme="light"] .highlight .no { color: #880000 } /* Name.Constant */
+html[data-theme="light"] .highlight .nd { color: #AA22FF } /* Name.Decorator */
+html[data-theme="light"] .highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
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+html[data-theme="light"] .highlight .nf { color: #0000FF } /* Name.Function */
+html[data-theme="light"] .highlight .nl { color: #767600 } /* Name.Label */
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+html[data-theme="dark"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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+html[data-theme="dark"] .highlight .nc { color: #00e0e0 } /* Name.Class */
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+html[data-theme="dark"] .highlight .se { color: #abe338 } /* Literal.String.Escape */
+html[data-theme="dark"] .highlight .sh { color: #abe338 } /* Literal.String.Heredoc */
+html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
+html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
+html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
+html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
+html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
+html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
+html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
+html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
+html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
+html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
+html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
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\ No newline at end of file
diff --git a/docs/_static/scripts/bootstrap.js b/docs/_static/scripts/bootstrap.js
new file mode 100644
index 00000000..766173ab
--- /dev/null
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@@ -0,0 +1,3 @@
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e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return Cs}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(de.off(this._config.target,_s),de.on(this._config.target,_s,ys,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Ft(e))continue;const t=ke.findOne(e.hash,this._element);Wt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),de.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Ws="fade",Fs="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),de.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?de.trigger(e,xs,{relatedTarget:t}):null;de.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),de.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Fs)}),t,t.classList.contains(Ws)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),de.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Fs)}),t,t.classList.contains(Ws)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Ft(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ke.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Hs),n(".dropdown-menu",Fs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(qs)?t:ke.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(document,$s,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this)||Vs.getOrCreateInstance(this).show()})),de.on(window,Is,(()=>{for(const t of ke.find(Rs))Vs.getOrCreateInstance(t)})),Qt(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends ye{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){de.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),qt(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),de.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(de.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),de.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){de.on(this._element,Qs,(t=>this._onInteraction(t,!0))),de.on(this._element,Xs,(t=>this._onInteraction(t,!1))),de.on(this._element,Ys,(t=>this._onInteraction(t,!0))),de.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}var ao;we(ro),Qt(ro),ao=function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new us(t,{delay:{show:500,hide:100}})}))},"loading"!=document.readyState?ao():document.addEventListener("DOMContentLoaded",ao)})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/docs/_static/scripts/bootstrap.js.LICENSE.txt b/docs/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 00000000..91ad10aa
--- /dev/null
+++ b/docs/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.2.3 (https://getbootstrap.com/)
+  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/docs/_static/scripts/bootstrap.js.map b/docs/_static/scripts/bootstrap.js.map
new file mode 100644
index 00000000..d83e2f7c
--- /dev/null
+++ b/docs/_static/scripts/bootstrap.js.map
@@ -0,0 +1 @@
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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (process.env.NODE_ENV !== \"production\") {\n    if (!isHTMLElement(arrowElement)) {\n      console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n    }\n\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n  if (process.env.NODE_ENV !== \"production\") {\n    var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n    if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n      return transitionProperty.indexOf(property) >= 0;\n    })) {\n      console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n    }\n  }\n\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n    }\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        }); // Validate the provided modifiers so that the consumer will get warned\n        // if one of the modifiers is invalid for any reason\n\n        if (process.env.NODE_ENV !== \"production\") {\n          var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n            var name = _ref.name;\n            return name;\n          });\n          validateModifiers(modifiers);\n\n          if (getBasePlacement(state.options.placement) === auto) {\n            var flipModifier = state.orderedModifiers.find(function (_ref2) {\n              var name = _ref2.name;\n              return name === 'flip';\n            });\n\n            if (!flipModifier) {\n              console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n            }\n          }\n\n          var _getComputedStyle = getComputedStyle(popper),\n              marginTop = _getComputedStyle.marginTop,\n              marginRight = _getComputedStyle.marginRight,\n              marginBottom = _getComputedStyle.marginBottom,\n              marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n          // cause bugs with positioning, so we'll warn the consumer\n\n\n          if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n            return parseFloat(margin);\n          })) {\n            console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n          }\n        }\n\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          if (process.env.NODE_ENV !== \"production\") {\n            console.error(INVALID_ELEMENT_ERROR);\n          }\n\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n        var __debug_loops__ = 0;\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (process.env.NODE_ENV !== \"production\") {\n            __debug_loops__ += 1;\n\n            if (__debug_loops__ > 100) {\n              console.error(INFINITE_LOOP_ERROR);\n              break;\n            }\n          }\n\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      if (process.env.NODE_ENV !== \"production\") {\n        console.error(INVALID_ELEMENT_ERROR);\n      }\n\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref3) {\n        var name = _ref3.name,\n            _ref3$options = _ref3.options,\n            options = _ref3$options === void 0 ? {} : _ref3$options,\n            effect = _ref3.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.2.3 (https://getbootstrap.com/)\n  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n\n  return prefix;\n};\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n\n  return selector;\n};\n\nconst getSelectorFromElement = element => {\n  const selector = getSelector(element);\n\n  if (selector) {\n    return document.querySelector(selector) ? selector : null;\n  }\n\n  return null;\n};\n\nconst getElementFromSelector = element => {\n  const selector = getSelector(element);\n  return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  } // Get transition-duration of the element\n\n\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  } // If multiple durations are defined, take the first\n\n\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n\n  return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(object);\n  }\n\n  return null;\n};\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n  const closedDetails = element.closest('details:not([open])');\n\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n\n    if (summary === null) {\n      return false;\n    }\n  }\n\n  return elementIsVisible;\n};\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  } // Can find the shadow root otherwise it'll return the document\n\n\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element;\n  } // when we don't find a shadow root\n\n\n  if (!element.parentNode) {\n    return null;\n  }\n\n  return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n\n  return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\n\nconst execute = callback => {\n  if (typeof callback === 'function') {\n    callback();\n  }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n\n  index += shouldGetNext ? 1 : -1;\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n\n    return fn.apply(element, [event]);\n  };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n\n  return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n\n    callable = wrapFunction(callable);\n  }\n\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n  if (!fn) {\n    return;\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n\n  for (const handlerKey of Object.keys(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      const event = storeElementEvent[handlerKey];\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n\n    for (const keyHandlers of Object.keys(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        const event = storeElementEvent[keyHandlers];\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n\n    let evt = new Event(event, {\n      bubbles,\n      cancelable: true\n    });\n    evt = hydrateObj(evt, args);\n\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n\n    return evt;\n  }\n\n};\n\nfunction hydrateObj(obj, meta) {\n  for (const [key, value] of Object.entries(meta || {})) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n\n        get() {\n          return value;\n        }\n\n      });\n    }\n  }\n\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n\n    const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n\n    instanceMap.set(key, instance);\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n\n    return null;\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n\n  if (value === 'false') {\n    return false;\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n\n  if (value === '' || value === 'null') {\n    return null;\n  }\n\n  if (typeof value !== 'string') {\n    return value;\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n\n    return attributes;\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n\n  static get DefaultType() {\n    return {};\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    return config;\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return { ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const property of Object.keys(configTypes)) {\n      const expectedTypes = configTypes[property];\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n\n    if (!element) {\n      return;\n    }\n\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  } // Public\n\n\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  } // Static\n\n\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n\n  static get VERSION() {\n    return VERSION;\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n\n    if (isDisabled(this)) {\n      return;\n    }\n\n    const target = getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  } // Public\n\n\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  } // Private\n\n\n  _destroyElement() {\n    this._element.remove();\n\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  } // Public\n\n\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n\n    return parents;\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n\n      previous = previous.previousElementSibling;\n    }\n\n    return [];\n  },\n\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n\n      next = next.nextElementSibling;\n    }\n\n    return [];\n  },\n\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n\n    this._initEvents();\n  } // Getters\n\n\n  static get Default() {\n    return Default$c;\n  }\n\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n\n  static get NAME() {\n    return NAME$d;\n  } // Public\n\n\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  } // Private\n\n\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n\n    this._handleSwipe();\n\n    execute(this._config.endCallback);\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n\n    if (!direction) {\n      return;\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  } // Static\n\n\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n    this._addEventListeners();\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$b;\n  }\n\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n\n  static get NAME() {\n    return NAME$c;\n  } // Public\n\n\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n\n    this._clearInterval();\n  }\n\n  cycle() {\n    this._clearInterval();\n\n    this._updateInterval();\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n\n    this.cycle();\n  }\n\n  to(index) {\n    const items = this._getItems();\n\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive());\n\n    if (activeIndex === index) {\n      return;\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n    this._slide(order, items[index]);\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      } // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n\n      this.pause();\n\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key];\n\n    if (direction) {\n      event.preventDefault();\n\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n\n    if (!element) {\n      return;\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n\n    const activeElement = this._getActive();\n\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n    if (nextElement === activeElement) {\n      return;\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement);\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // todo: change tests that use empty divs to avoid this check\n      return;\n    }\n\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n\n    this._setActiveIndicatorElement(nextElementIndex);\n\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n\n  if (slideIndex) {\n    carousel.to(slideIndex);\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  carousel.prev();\n\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n    for (const elem of toggleList) {\n      const selector = getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n\n    this._initializeChildren();\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$a;\n  }\n\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n\n  static get NAME() {\n    return NAME$b;\n  } // Public\n\n\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n\n    let activeChildren = []; // find active children\n\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.style[dimension] = 0;\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n\n    this._queueCallback(complete, this._element, true);\n\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n    for (const trigger of this._triggerArray) {\n      const element = getElementFromSelector(trigger);\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n\n    this._element.style[dimension] = '';\n\n    this._queueCallback(complete, this._element, true);\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n\n    config.parent = getElement(config.parent);\n    return config;\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n    for (const element of children) {\n      const selected = getElementFromSelector(element);\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    const _config = {};\n\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n\n  const selector = getSelectorFromElement(this);\n  const selectorElements = SelectorEngine.find(selector);\n\n  for (const element of selectorElements) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  } // Getters\n\n\n  static get Default() {\n    return Default$9;\n  }\n\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n\n  static get NAME() {\n    return NAME$a;\n  } // Public\n\n\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._createPopper(); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    this._element.focus();\n\n    this._element.setAttribute('aria-expanded', true);\n\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n\n    this._completeHide(relatedTarget);\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    super.dispose();\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar();\n\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Private\n\n\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    } // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.setAttribute('aria-expanded', 'false');\n\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config);\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n\n    return config;\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n\n    let referenceElement = this._element;\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n\n    const popperConfig = this._getPopperConfig();\n\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent;\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    } // We need to trim the value because custom properties can also include spaces\n\n\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n    if (!items.length) {\n      return;\n    } // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n\n      context._completeHide(relatedTarget);\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n\n    event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n\n      instance._selectMenuItem(event);\n\n      return;\n    }\n\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  } // Public\n\n\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n\n  hide() {\n    const width = this.getWidth();\n\n    this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0;\n  } // Private\n\n\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n\n    this._element.style.overflow = 'hidden';\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n\n      this._saveInitialAttribute(element, styleProperty);\n\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$8;\n  }\n\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n\n  static get NAME() {\n    return NAME$9;\n  } // Public\n\n\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._append();\n\n    const element = this._getElement();\n\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n\n    element.classList.add(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n    this._element.remove();\n\n    this._isAppended = false;\n  } // Private\n\n\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n\n      this._element = backdrop;\n    }\n\n    return this._element;\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n\n    const element = this._getElement();\n\n    this._config.rootElement.append(element);\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$7;\n  }\n\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n\n  static get NAME() {\n    return NAME$8;\n  } // Public\n\n\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  } // Private\n\n\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement);\n\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$6;\n  }\n\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n\n  static get NAME() {\n    return NAME$7;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n    this._isTransitioning = true;\n\n    this._scrollBar.hide();\n\n    document.body.classList.add(CLASS_NAME_OPEN);\n\n    this._adjustDialog();\n\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = false;\n    this._isTransitioning = true;\n\n    this._focustrap.deactivate();\n\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n\n  dispose() {\n    for (const htmlElement of [window, this._dialog]) {\n      EventHandler.off(htmlElement, EVENT_KEY$4);\n    }\n\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  }\n\n  handleUpdate() {\n    this._adjustDialog();\n  } // Private\n\n\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n\n    this._element.style.display = 'block';\n\n    this._element.removeAttribute('aria-hidden');\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n\n      if (this._config.keyboard) {\n        event.preventDefault();\n        this.hide();\n        return;\n      }\n\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n\n          return;\n        }\n\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none';\n\n    this._element.setAttribute('aria-hidden', true);\n\n    this._element.removeAttribute('aria-modal');\n\n    this._element.removeAttribute('role');\n\n    this._isTransitioning = false;\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n\n      this._resetAdjustments();\n\n      this._scrollBar.reset();\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC);\n\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n\n    this._element.focus();\n  }\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n    const scrollbarWidth = this._scrollBar.getWidth();\n\n    const isBodyOverflowing = scrollbarWidth > 0;\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  } // Static\n\n\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](relatedTarget);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  }); // avoid conflict when clicking modal toggler while another one is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$5;\n  }\n\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n\n  static get NAME() {\n    return NAME$6;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n\n    this._backdrop.show();\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._focustrap.deactivate();\n\n    this._element.blur();\n\n    this._isShown = false;\n\n    this._element.classList.add(CLASS_NAME_HIDING);\n\n    this._backdrop.hide();\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n      this._element.removeAttribute('aria-modal');\n\n      this._element.removeAttribute('role');\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n\n    this._queueCallback(completeCallback, this._element, true);\n  }\n\n  dispose() {\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  } // Private\n\n\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n\n      if (!this._config.keyboard) {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    });\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  if (isDisabled(this)) {\n    return;\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n    }\n\n    return true;\n  } // Check if a regular expression validates the attribute.\n\n\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  } // Getters\n\n\n  static get Default() {\n    return Default$4;\n  }\n\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n\n  static get NAME() {\n    return NAME$5;\n  } // Public\n\n\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n\n  changeContent(content) {\n    this._checkContent(content);\n\n    this._config.content = { ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n\n    const template = templateWrapper.children[0];\n\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n\n    return template;\n  } // Private\n\n\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n\n    this._checkContent(config.content);\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n\n    if (!templateElement) {\n      return;\n    }\n\n    content = this._resolvePossibleFunction(content);\n\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n\n      return;\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n\n    templateElement.textContent = content;\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg(this) : arg;\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n\n    templateElement.textContent = element.textContent;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 0],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n\n    super(element, config); // Private\n\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null; // Protected\n\n    this.tip = null;\n\n    this._setListeners();\n\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$3;\n  }\n\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n\n  static get NAME() {\n    return NAME$4;\n  } // Public\n\n\n  enable() {\n    this._isEnabled = true;\n  }\n\n  disable() {\n    this._isEnabled = false;\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click;\n\n    if (this._isShown()) {\n      this._leave();\n\n      return;\n    }\n\n    this._enter();\n  }\n\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n\n    this._disposePopper();\n\n    super.dispose();\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    } // todo v6 remove this OR make it optional\n\n\n    this._disposePopper();\n\n    const tip = this._getTipElement();\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n    const {\n      container\n    } = this._config;\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n      if (this._isHovered === false) {\n        this._leave();\n      }\n\n      this._isHovered = false;\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const tip = this._getTipElement();\n\n    tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n\n      this._element.removeAttribute('aria-describedby');\n\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Protected\n\n\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n\n    return this.tip;\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n    if (!tip) {\n      return null;\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n\n    return tip;\n  }\n\n  setContent(content) {\n    this._newContent = content;\n\n    if (this._isShown()) {\n      this._disposePopper();\n\n      this.show();\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({ ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n\n    return this._templateFactory;\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  } // Private\n\n\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n\n  _createPopper(tip) {\n    const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg.call(this._element) : arg;\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n          context._leave();\n        });\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n\n    if (!title) {\n      return;\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n    this._element.removeAttribute('title');\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n\n    this._isHovered = true;\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n\n    this._isHovered = false;\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n\n    config = { ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n\n    return config;\n  }\n\n  _getDelegateConfig() {\n    const config = {};\n\n    for (const key in this._config) {\n      if (this.constructor.Default[key] !== this._config[key]) {\n        config[key] = this._config[key];\n      }\n    }\n\n    config.selector = false;\n    config.trigger = 'manual'; // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n\n    return config;\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n\n      this._popper = null;\n    }\n\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n\n  static get NAME() {\n    return NAME$3;\n  } // Overrides\n\n\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  } // Private\n\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  } // Getters\n\n\n  static get Default() {\n    return Default$1;\n  }\n\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n\n  static get NAME() {\n    return NAME$2;\n  } // Public\n\n\n  refresh() {\n    this._initializeTargetsAndObservables();\n\n    this._maybeEnableSmoothScroll();\n\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect();\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n\n    return config;\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    } // unregister any previous listeners\n\n\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        } // Chrome 60 doesn't support `scrollTo`\n\n\n        root.scrollTop = height;\n      }\n    });\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  } // The logic of selection\n\n\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n      this._process(targetElement(entry));\n    };\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n\n        this._clearActiveClass(targetElement(entry));\n\n        continue;\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n        if (!parentScrollTop) {\n          return;\n        }\n\n        continue;\n      } // if we are scrolling up, pick the smallest offsetTop\n\n\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n\n      const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(anchor.hash, anchor);\n\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n\n    this._clearActiveClass(this._config.target);\n\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n\n    this._activateParents(target);\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)';\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // todo:v6: could be only `tab`\n\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n\n    if (!this._parent) {\n      return; // todo: should Throw exception on v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    } // Set up initial aria attributes\n\n\n    this._setInitialAttributes(this._parent, this._getChildren());\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  } // Getters\n\n\n  static get NAME() {\n    return NAME$1;\n  } // Public\n\n\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n\n    if (this._elemIsActive(innerElem)) {\n      return;\n    } // Search for active tab on same parent to deactivate it\n\n\n    const active = this._getActiveElem();\n\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._deactivate(active, innerElem);\n\n    this._activate(innerElem, active);\n  } // Private\n\n\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE);\n\n    this._activate(getElementFromSelector(element)); // Search and activate/show the proper section\n\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n\n      this._toggleDropDown(element, true);\n\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n\n    this._deactivate(getElementFromSelector(element)); // Search and deactivate the shown section too\n\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n\n      this._toggleDropDown(element, false);\n\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)) {\n      return;\n    }\n\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n\n    event.preventDefault();\n    const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n    const nextActiveElement = getNextActiveElement(this._getChildren().filter(element => !isDisabled(element)), event.target, isNext, true);\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n\n    const isActive = this._elemIsActive(child);\n\n    const outerElem = this._getOuterElement(child);\n\n    child.setAttribute('aria-selected', isActive);\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab'); // set attributes to the related panel too\n\n\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = getElementFromSelector(child);\n\n    if (!target) {\n      return;\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `#${child.id}`);\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  } // Try to get the inner element (usually the .nav-link)\n\n\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  } // Try to get the outer element (usually the .nav-item)\n\n\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  if (isDisabled(this)) {\n    return;\n  }\n\n  Tab.getOrCreateInstance(this).show();\n});\n/**\n * Initialize on focus\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\n\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n\n    this._setListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default;\n  }\n\n  static get DefaultType() {\n    return DefaultType;\n  }\n\n  static get NAME() {\n    return NAME;\n  } // Public\n\n\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._clearTimeout();\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n\n      this._maybeScheduleHide();\n    };\n\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n\n\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n\n\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n\n    this._element.classList.add(CLASS_NAME_SHOWING);\n\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n\n  dispose() {\n    this._clearTimeout();\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n\n    super.dispose();\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  } // Private\n\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout();\n\n      return;\n    }\n\n    const nextElement = event.relatedTarget;\n\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n\n    this._maybeScheduleHide();\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config](this);\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Toast);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// Import and setup functions to control Bootstrap's behavior.\nimport 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\"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \"div:not(.search-button__search-container) > form.bd-search\"\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  let input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  let searchPopupWrapper = document.querySelector(\".search-button__wrapper\");\n  let hiddenInput = searchPopupWrapper.querySelector(\"input\");\n  if (input === hiddenInput) {\n    searchPopupWrapper.classList.toggle(\"show\");\n  }\n  // when toggling off the search field, remove its focus\n  if (document.activeElement === input) {\n    input.blur();\n  } else {\n    input.focus();\n    input.select();\n    input.scrollIntoView({ block: \"center\" });\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if ((event.ctrlKey || event.metaKey) && event.code == \"KeyK\") {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && event.code == \"Escape\") {\n        toggleSearchField();\n      }\n    },\n    true\n  );\n};\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\nvar changeSearchShortcutKey = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  var isMac = window.navigator.platform.toUpperCase().indexOf(\"MAC\") >= 0;\n  if (isMac) {\n    forms.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\")\n    );\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // Add the search button overlay event callback\n  let overlay = document.querySelector(\".search-button__overlay\");\n  if (overlay) {\n    overlay.onclick = toggleSearchField;\n  }\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nfunction checkPageExistsAndRedirect(event) {\n  const currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`,\n    tryUrl = event.target.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n\n  fetch(tryUrl, { method: \"HEAD\" })\n    .then(() => {\n      location.href = tryUrl;\n    }) // if the page exists, go there\n    .catch((error) => {\n      location.href = otherDocsHomepage;\n    });\n\n  // this prevents the browser from following the href of the clicked node\n  // (which is fine because this function takes care of redirecting)\n  return false;\n}\n\n// Populate the version switcher from the JSON config file\nvar themeSwitchBtns = document.querySelectorAll(\".version-switcher__button\");\nif (themeSwitchBtns.length) {\n  fetch(DOCUMENTATION_OPTIONS.theme_switcher_json_url)\n    .then((res) => {\n      return res.json();\n    })\n    .then((data) => {\n      const currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`;\n      themeSwitchBtns.forEach((btn) => {\n        // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n        btn.dataset[\"activeVersionName\"] = \"\";\n        btn.dataset[\"activeVersion\"] = \"\";\n      });\n      // create links to the corresponding page in the other docs versions\n      data.forEach((entry) => {\n        // if no custom name specified (e.g., \"latest\"), use version string\n        if (!(\"name\" in entry)) {\n          entry.name = entry.version;\n        }\n        // create the node\n        const span = document.createElement(\"span\");\n        span.textContent = `${entry.name}`;\n\n        const node = document.createElement(\"a\");\n        node.setAttribute(\n          \"class\",\n          \"list-group-item list-group-item-action py-1\"\n        );\n        node.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n        node.appendChild(span);\n\n        // on click, AJAX calls will check if the linked page exists before\n        // trying to redirect, and if not, will redirect to the homepage\n        // for that version of the docs.\n        node.onclick = checkPageExistsAndRedirect;\n        // Add dataset values for the version and name in case people want\n        // to apply CSS styling based on this information.\n        node.dataset[\"versionName\"] = entry.name;\n        node.dataset[\"version\"] = entry.version;\n\n        document.querySelector(\".version-switcher__menu\").append(node);\n        // replace dropdown button text with the preferred display name of\n        // this version, rather than using sphinx's {{ version }} variable.\n        // also 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(mutation.addedNodes.length === 0) {\n        return;\n      }\n      if (mutation.addedNodes[0].data === undefined) {\n        return;\n      }\n      if (mutation.addedNodes[0].data.search(\"Inserted RTD Footer\") != -1) {\n        mutation.addedNodes.forEach((node) => {\n          document.getElementById(\"rtd-footer-container\").append(node);\n        });\n      }\n    });\n  };\n\n  const observer = new MutationObserver(mutatedCallback);\n  const config = { childList: true };\n  observer.observe(document.body, config);\n}\n\n/*******************************************************************************\n * Call functions after document loading.\n 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\ No newline at end of file
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
new file mode 100644
index 00000000..e89e34d4
--- /dev/null
+++ b/docs/_static/searchtools.js
@@ -0,0 +1,566 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = docUrlRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = docUrlRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr)
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/docs/_static/sg_gallery-binder.css b/docs/_static/sg_gallery-binder.css
new file mode 100644
index 00000000..a33aa420
--- /dev/null
+++ b/docs/_static/sg_gallery-binder.css
@@ -0,0 +1,6 @@
+/* CSS for binder integration */
+
+div.binder-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/docs/_static/sg_gallery-dataframe.css b/docs/_static/sg_gallery-dataframe.css
new file mode 100644
index 00000000..25be7309
--- /dev/null
+++ b/docs/_static/sg_gallery-dataframe.css
@@ -0,0 +1,46 @@
+/* Pandas dataframe css */
+/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-tr-odd-color: #f5f5f5;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-tr-odd-color: #373737;
+  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
+}
+
+table.dataframe {
+  border: none !important;
+  border-collapse: collapse;
+  border-spacing: 0;
+  border-color: transparent;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+table.dataframe thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+table.dataframe tr,
+table.dataframe th,
+table.dataframe td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+table.dataframe th {
+  font-weight: bold;
+}
+table.dataframe tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+table.dataframe tbody tr:hover {
+  background: var(--sg-tr-hover-color);
+}
diff --git a/docs/_static/sg_gallery-rendered-html.css b/docs/_static/sg_gallery-rendered-html.css
new file mode 100644
index 00000000..93dc2ffb
--- /dev/null
+++ b/docs/_static/sg_gallery-rendered-html.css
@@ -0,0 +1,224 @@
+/* Adapted from notebook/static/style/style.min.css */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-background-color: #ffffff;
+  --sg-code-background-color: #eff0f1;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #f5f5f5;
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-background-color: #121212;
+  --sg-code-background-color: #2f2f30;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #1f1f1f;
+}
+
+.rendered_html {
+  color: var(--sg-text-color);
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: var(--sg-text-color);
+  background-color: var(--sg-text-color);
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: var(--sg-background-color);
+}
+.rendered_html code {
+  background-color: var(--sg-code-background-color);
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: var(--sg-background-color);
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: var(--sg-text-color);
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+.rendered_html tbody tr:hover {
+  color: var(--sg-text-color);
+  background: var(--sg-tr-hover-color);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/docs/_static/sg_gallery.css b/docs/_static/sg_gallery.css
new file mode 100644
index 00000000..72227837
--- /dev/null
+++ b/docs/_static/sg_gallery.css
@@ -0,0 +1,342 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+
+/* Define light colors */
+:root, html[data-theme="light"], body[data-theme="light"]{
+  --sg-tooltip-foreground: black;
+  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+  --sg-tooltip-border: #ccc transparent;
+  --sg-thumb-box-shadow-color: #6c757d40;
+  --sg-thumb-hover-border: #0069d9;
+  --sg-script-out: #888;
+  --sg-script-pre: #fafae2;
+  --sg-pytb-foreground: #000;
+  --sg-pytb-background: #ffe4e4;
+  --sg-pytb-border-color: #f66;
+  --sg-download-a-background-color: #ffc;
+  --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+  --sg-download-a-border-color: 1px solid #c2c22d;
+  --sg-download-a-color: #000;
+  --sg-download-a-hover-background-color: #d5d57e;
+  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+}
+@media(prefers-color-scheme: light) {
+  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: black;
+    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+    --sg-tooltip-border: #ccc transparent;
+    --sg-thumb-box-shadow-color: #6c757d40;
+    --sg-thumb-hover-border: #0069d9;
+    --sg-script-out: #888;
+    --sg-script-pre: #fafae2;
+    --sg-pytb-foreground: #000;
+    --sg-pytb-background: #ffe4e4;
+    --sg-pytb-border-color: #f66;
+    --sg-download-a-background-color: #ffc;
+    --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+    --sg-download-a-border-color: 1px solid #c2c22d;
+    --sg-download-a-color: #000;
+    --sg-download-a-hover-background-color: #d5d57e;
+    --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+  }
+}
+
+html[data-theme="dark"], body[data-theme="dark"] {
+  --sg-tooltip-foreground: white;
+  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+  --sg-tooltip-border: #333 transparent;
+  --sg-thumb-box-shadow-color: #79848d40;
+  --sg-thumb-hover-border: #003975;
+  --sg-script-out: rgb(179, 179, 179);
+  --sg-script-pre: #2e2e22;
+  --sg-pytb-foreground: #fff;
+  --sg-pytb-background: #1b1717;
+  --sg-pytb-border-color: #622;
+  --sg-download-a-background-color: #443;
+  --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+  --sg-download-a-border-color: 1px solid #3a3a0d;
+  --sg-download-a-color: #fff;
+  --sg-download-a-hover-background-color: #616135;
+  --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+}
+@media(prefers-color-scheme: dark){
+  html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: white;
+    --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+    --sg-tooltip-border: #333 transparent;
+    --sg-thumb-box-shadow-color: #79848d40;
+    --sg-thumb-hover-border: #003975;
+    --sg-script-out: rgb(179, 179, 179);
+    --sg-script-pre: #2e2e22;
+    --sg-pytb-foreground: #fff;
+    --sg-pytb-background: #1b1717;
+    --sg-pytb-border-color: #622;
+    --sg-download-a-background-color: #443;
+    --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+    --sg-download-a-border-color: 1px solid #3a3a0d;
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: #616135;
+    --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+  }
+}
+
+.sphx-glr-thumbnails {
+  width: 100%;
+  margin: 0px 0px 20px 0px;
+
+  /* align thumbnails on a grid */
+  justify-content: space-between;
+  display: grid;
+  /* each grid column should be at least 160px (this will determine
+  the actual number of columns) and then take as much of the
+  remaining width as possible */
+  grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+  gap: 15px;
+}
+.sphx-glr-thumbnails .toctree-wrapper {
+  /* hide empty toctree divs added to the DOM
+  by sphinx even though the toctree is hidden
+  (they would fill grid places with empty divs) */
+  display: none;
+}
+.sphx-glr-thumbcontainer {
+  background: transparent;
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color);
+
+  /* useful to absolutely position link in div */
+  position: relative;
+
+  /* thumbnail width should include padding and borders
+  and take all available space */
+  box-sizing: border-box;
+  width: 100%;
+  padding: 10px;
+  border: 1px solid transparent;
+
+  /* align content in thumbnail */
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 7px;
+}
+.sphx-glr-thumbcontainer p {
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+.sphx-glr-thumbcontainer p,
+.sphx-glr-thumbcontainer p a {
+  /* link should cover the whole thumbnail div */
+  width: 100%;
+  height: 100%;
+}
+.sphx-glr-thumbcontainer p a span {
+  /* text within link should be masked
+  (we are just interested in the href) */
+  display: none;
+}
+.sphx-glr-thumbcontainer:hover {
+  border: 1px solid;
+  border-color: var(--sg-thumb-hover-border);
+  cursor: pointer;
+}
+.sphx-glr-thumbcontainer a.internal {
+  bottom: 0;
+  display: block;
+  left: 0;
+  box-sizing: border-box;
+  padding: 150px 10px 0;
+  position: absolute;
+  right: 0;
+  top: 0;
+}
+/* Next one is to avoid Sphinx traditional theme to cover all the
+thumbnail with its default link Background color */
+.sphx-glr-thumbcontainer a.internal:hover {
+  background-color: transparent;
+}
+
+.sphx-glr-thumbcontainer p {
+  margin: 0 0 0.1em 0;
+}
+.sphx-glr-thumbcontainer .figure {
+  margin: 10px;
+  width: 160px;
+}
+.sphx-glr-thumbcontainer img {
+  display: inline;
+  max-height: 112px;
+  max-width: 160px;
+}
+.sphx-glr-thumbcontainer[tooltip]:hover:after {
+  background: var(--sg-tooltip-background);
+  -webkit-border-radius: 4px;
+  -moz-border-radius: 4px;
+  border-radius: 4px;
+  color: var(--sg-tooltip-foreground);
+  content: attr(tooltip);
+  padding: 10px;
+  z-index: 98;
+  width: 100%;
+  height: 100%;
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  box-sizing: border-box;
+  overflow: hidden;
+  backdrop-filter: blur(3px);
+}
+
+.sphx-glr-script-out {
+  color: var(--sg-script-out);
+  display: flex;
+  gap: 0.5em;
+}
+.sphx-glr-script-out::before {
+  content: "Out:";
+  /* These numbers come from the pre style in the pydata sphinx theme. This
+   * turns out to match perfectly on the rtd theme, but be a bit too low for
+   * the pydata sphinx theme. As I could not find a dimension to use that was
+   * scaled the same way, I just picked one option that worked pretty close for
+   * both. */
+  line-height: 1.4;
+  padding-top: 10px;
+}
+.sphx-glr-script-out .highlight {
+  background-color: transparent;
+  /* These options make the div expand... */
+  flex-grow: 1;
+  /* ... but also keep it from overflowing its flex container. */
+  overflow: auto;
+}
+.sphx-glr-script-out .highlight pre {
+  background-color: var(--sg-script-pre);
+  border: 0;
+  max-height: 30em;
+  overflow: auto;
+  padding-left: 1ex;
+  /* This margin is necessary in the pydata sphinx theme because pre has a box
+   * shadow which would be clipped by the overflow:auto in the parent div
+   * above. */
+  margin: 2px;
+  word-break: break-word;
+}
+.sphx-glr-script-out + p {
+  margin-top: 1.8em;
+}
+blockquote.sphx-glr-script-out {
+  margin-left: 0pt;
+}
+.sphx-glr-script-out.highlight-pytb .highlight pre {
+  color: var(--sg-pytb-foreground);
+  background-color: var(--sg-pytb-background);
+  border: 1px solid var(--sg-pytb-border-color);
+  margin-top: 10px;
+  padding: 7px;
+}
+
+div.sphx-glr-footer {
+  text-align: center;
+}
+
+div.sphx-glr-download {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.sphx-glr-download a {
+  background-color: var(--sg-download-a-background-color);
+  background-image: var(--sg-download-a-background-image);
+  border-radius: 4px;
+  border: 1px solid var(--sg-download-a-border-color);
+  color: var(--sg-download-a-color);
+  display: inline-block;
+  font-weight: bold;
+  padding: 1ex;
+  text-align: center;
+}
+
+div.sphx-glr-download code.download {
+  display: inline-block;
+  white-space: normal;
+  word-break: normal;
+  overflow-wrap: break-word;
+  /* border and background are given by the enclosing 'a' */
+  border: none;
+  background: none;
+}
+
+div.sphx-glr-download a:hover {
+  box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2);
+  text-decoration: none;
+  background-image: none;
+  background-color: var(--sg-download-a-hover-background-color);
+}
+
+.sphx-glr-example-title:target::before {
+  display: block;
+  content: "";
+  margin-top: -50px;
+  height: 50px;
+  visibility: hidden;
+}
+
+ul.sphx-glr-horizontal {
+  list-style: none;
+  padding: 0;
+}
+ul.sphx-glr-horizontal li {
+  display: inline;
+}
+ul.sphx-glr-horizontal img {
+  height: auto !important;
+}
+
+.sphx-glr-single-img {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+
+.sphx-glr-multi-img {
+  max-width: 42%;
+  height: auto;
+}
+
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation {
+  display: block;
+}
+
+p.sphx-glr-signature a.reference.external {
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  padding: 3px;
+  font-size: 75%;
+  text-align: right;
+  margin-left: auto;
+  display: table;
+}
+
+.sphx-glr-clear {
+  clear: both;
+}
+
+a.sphx-glr-backref-instance {
+  text-decoration: none;
+}
diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js
new file mode 100644
index 00000000..aae669d7
--- /dev/null
+++ b/docs/_static/sphinx_highlight.js
@@ -0,0 +1,144 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(SphinxHighlight.highlightSearchWords);
+_ready(SphinxHighlight.initEscapeListener);
diff --git a/docs/_static/styles/bootstrap.css b/docs/_static/styles/bootstrap.css
new file mode 100644
index 00000000..0aa5f54f
--- /dev/null
+++ b/docs/_static/styles/bootstrap.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap  v5.2.3 (https://getbootstrap.com/)
+ * Copyright 2011-2022 The Bootstrap Authors
+ * Copyright 2011-2022 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */:root{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-body-color-rgb:33,37,41;--bs-body-bg-rgb:255,255,255;--bs-font-sans-serif:system-ui,-apple-system,"Segoe 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var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:#fff;--bs-pagination-border-width:1px;--bs-pagination-border-color:#dee2e6;--bs-pagination-border-radius:0.375rem;--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:#e9ecef;--bs-pagination-hover-border-color:#dee2e6;--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:#e9ecef;--bs-pagination-focus-box-shadow:0 0 0 0.25rem 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diff --git a/docs/_static/styles/pydata-sphinx-theme.css b/docs/_static/styles/pydata-sphinx-theme.css
new file mode 100644
index 00000000..ca295d8a
--- /dev/null
+++ b/docs/_static/styles/pydata-sphinx-theme.css
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/_static/styles/theme.css b/docs/_static/styles/theme.css
new file mode 100644
index 00000000..4519dd91
--- /dev/null
+++ b/docs/_static/styles/theme.css
@@ -0,0 +1,2 @@
+/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */
+@import "../basic.css";
diff --git a/docs/_static/underscore-1.13.1.js b/docs/_static/underscore-1.13.1.js
new file mode 100644
index 00000000..ffd77af9
--- /dev/null
+++ b/docs/_static/underscore-1.13.1.js
@@ -0,0 +1,2042 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.13.1
+  //     https://underscorejs.org
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.13.1';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has$1(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has$1(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
+    this._wrapped = obj;
+  }
+
+  _$1.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _$1.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
+
+  _$1.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath$1(path) {
+    return isArray(path) ? path : [path];
+  }
+  _$1.toPath = toPath$1;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath(path) {
+    return _$1.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has(obj, path) {
+    path = toPath(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has$1(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _$1.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _$1.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _$1.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(argument, '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _$1);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _$1(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _$1;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten$1(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten$1(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten$1(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _$1, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten$1(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten$1(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten$1(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten$1(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _$1(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_$1, args));
+      };
+    });
+    return _$1;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath$1,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _$1
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _ = mixin(allExports);
+  // Legacy Node.js API.
+  _._ = _;
+
+  return _;
+
+})));
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/_static/underscore.js b/docs/_static/underscore.js
new file mode 100644
index 00000000..cf177d42
--- /dev/null
+++ b/docs/_static/underscore.js
@@ -0,0 +1,6 @@
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.13.1
+//     https://underscorejs.org
+//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
+var n="1.13.1",r="object"==typeof self&&self.self===self&&self||"object"==typeof global&&global.global===global&&global||Function("return this")()||{},t=Array.prototype,e=Object.prototype,u="undefined"!=typeof Symbol?Symbol.prototype:null,o=t.push,i=t.slice,a=e.toString,f=e.hasOwnProperty,c="undefined"!=typeof ArrayBuffer,l="undefined"!=typeof DataView,s=Array.isArray,p=Object.keys,v=Object.create,h=c&&ArrayBuffer.isView,y=isNaN,d=isFinite,g=!{toString:null}.propertyIsEnumerable("toString"),b=["valueOf","isPrototypeOf","toString","propertyIsEnumerable","hasOwnProperty","toLocaleString"],m=Math.pow(2,53)-1;function j(n,r){return r=null==r?n.length-1:+r,function(){for(var t=Math.max(arguments.length-r,0),e=Array(t),u=0;u<t;u++)e[u]=arguments[u+r];switch(r){case 0:return n.call(this,e);case 1:return n.call(this,arguments[0],e);case 2:return n.call(this,arguments[0],arguments[1],e)}var o=Array(r+1);for(u=0;u<r;u++)o[u]=arguments[u];return o[r]=e,n.apply(this,o)}}function _(n){var r=typeof 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diff --git a/docs/_static/vendor/fontawesome/6.1.2/LICENSE.txt b/docs/_static/vendor/fontawesome/6.1.2/LICENSE.txt
new file mode 100644
index 00000000..cc557ece
--- /dev/null
+++ b/docs/_static/vendor/fontawesome/6.1.2/LICENSE.txt
@@ -0,0 +1,165 @@
+Fonticons, Inc. (https://fontawesome.com)
+
+--------------------------------------------------------------------------------
+
+Font Awesome Free License
+
+Font Awesome Free is free, open source, and GPL friendly. You can use it for
+commercial projects, open source projects, or really almost whatever you want.
+Full Font Awesome Free license: https://fontawesome.com/license/free.
+
+--------------------------------------------------------------------------------
+
+# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
+
+The Font Awesome Free download is licensed under a Creative Commons
+Attribution 4.0 International License and applies to all icons packaged
+as SVG and JS file types.
+
+--------------------------------------------------------------------------------
+
+# Fonts: SIL OFL 1.1 License
+
+In the Font Awesome Free download, the SIL OFL license applies to all icons
+packaged as web and desktop font files.
+
+Copyright (c) 2022 Fonticons, Inc. (https://fontawesome.com)
+with Reserved Font Name: "Font Awesome".
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+http://scripts.sil.org/OFL
+
+SIL OPEN FONT LICENSE
+Version 1.1 - 26 February 2007
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded,
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting — in part or in whole — any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Code: MIT License (https://opensource.org/licenses/MIT)
+
+In the Font Awesome Free download, the MIT license applies to all non-font and
+non-icon files.
+
+Copyright 2022 Fonticons, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in the
+Software without restriction, including without limitation the rights to use, copy,
+modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+and to permit persons to whom the Software is furnished to do so, subject to the
+following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Attribution
+
+Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
+Awesome Free files already contain embedded comments with sufficient
+attribution, so you shouldn't need to do anything additional when using these
+files normally.
+
+We've kept attribution comments terse, so we ask that you do not actively work
+to remove them from files, especially code. They're a great way for folks to
+learn about Font Awesome.
+
+--------------------------------------------------------------------------------
+
+# Brand Icons
+
+All brand icons are trademarks of their respective owners. The use of these
+trademarks does not indicate endorsement of the trademark holder by Font
+Awesome, nor vice versa. **Please do not use brand logos for any purpose except
+to represent the company, product, or service to which they refer.**
diff --git a/docs/_static/vendor/fontawesome/6.1.2/css/all.min.css b/docs/_static/vendor/fontawesome/6.1.2/css/all.min.css
new file mode 100644
index 00000000..aad4b1d9
--- /dev/null
+++ b/docs/_static/vendor/fontawesome/6.1.2/css/all.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2022 Fonticons, Inc.
+ */.fa{font-family:var(--fa-style-family,"Font Awesome 6 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diff --git a/docs/_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.ttf b/docs/_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.ttf
new file mode 100644
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diff --git a/docs/_static/webpack-macros.html b/docs/_static/webpack-macros.html
new file mode 100644
index 00000000..58db7859
--- /dev/null
+++ b/docs/_static/webpack-macros.html
@@ -0,0 +1,30 @@
+<!--
+  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
+  These are re-used in layout.html
+-->
+{# Load FontAwesome icons #}
+{% macro head_pre_icons() %}
+  <link href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}" />
+{% endmacro %}
+
+{% macro head_pre_assets() %}
+  <!-- Loaded before other Sphinx assets -->
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=e353d410970836974a52" />
+<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=e353d410970836974a52" />
+{% endmacro %}
+
+{% macro body_post() %}
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=e353d410970836974a52"></script>
+<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=e353d410970836974a52"></script>
+{% endmacro %}
\ No newline at end of file
diff --git a/docs/analysis.html b/docs/analysis.html
new file mode 100644
index 00000000..45b02e82
--- /dev/null
+++ b/docs/analysis.html
@@ -0,0 +1,581 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="_static/moabb_logo.png" type="image/png">
+    <title>Analysis &#8212; moabb 1.0.0-dev documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
+  
+  <!-- Loaded before other Sphinx assets -->
+  <link href="_static/styles/theme.css?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="_static/styles/bootstrap.css?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="_static/styles/pydata-sphinx-theme.css?digest=e353d410970836974a52" rel="stylesheet" />
+
+  
+  <link href="_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=e353d410970836974a52" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
+
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
+    <link rel="stylesheet" type="text/css" href="_static/design-style.4045f2051d55cab465a707391d5b2007.min.css" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
+    <link rel="stylesheet" type="text/css" href="_static/css/custom.css" />
+    <link rel="stylesheet" type="text/css" href="https://cdn.datatables.net/v/dt/dt-2.0.4/b-3.0.2/b-html5-3.0.2/datatables.min.css" />
+  
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=e353d410970836974a52" />
+<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=e353d410970836974a52" />
+
+    <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+    <script src="_static/jquery.js"></script>
+    <script src="_static/underscore.js"></script>
+    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+    <script src="_static/doctools.js"></script>
+    <script src="_static/sphinx_highlight.js"></script>
+    <script src="_static/clipboard.min.js"></script>
+    <script src="_static/copybutton.js"></script>
+    <script src="_static/design-tabs.js"></script>
+    <script async="async" src="https://www.googletagmanager.com/gtag/js?id=G-5WJBKDMSTE"></script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-5WJBKDMSTE');
+            </script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'analysis';</script>
+    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
+    <script src="https://cdn.datatables.net/v/dt/dt-2.0.4/b-3.0.2/b-html5-3.0.2/datatables.min.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+  </head>
+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
+
+  
+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
+  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__secondary"
+          id="__secondary"/>
+  <label class="overlay overlay-secondary" for="__secondary"></label>
+  
+  <div class="search-button__wrapper">
+    <div class="search-button__overlay"></div>
+    <div class="search-button__search-container">
+<form class="bd-search d-flex align-items-center"
+      action="search.html"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         id="search-input"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+  </div>
+
+
+<script>
+document.write(`<div class="bd-header-announcement"></div>`);
+fetch("https://raw.githubusercontent.com/NeuroTechX/moabb/develop/docs/source/_templates/custom-template.html")
+  .then(res => {return res.text();})
+  .then(data => {
+    div = document.querySelector(".bd-header-announcement");
+    div.classList.add(...["bd-header-announcement", "container-fluid"]);
+    div.innerHTML = `<div class="bd-header-announcement__content">${data}</div>`;
+  })
+  .catch(error => {
+    console.log("[PST]: Failed to load announcement at: https://raw.githubusercontent.com/NeuroTechX/moabb/develop/docs/source/_templates/custom-template.html");
+  });
+</script>
+  
+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
+<div class="bd-header__inner bd-page-width">
+  <label class="sidebar-toggle primary-toggle" for="__primary">
+    <span class="fa-solid fa-bars"></span>
+  </label>
+  
+  <div class="navbar-header-items__start">
+    
+      <div class="navbar-item">
+  
+
+<a class="navbar-brand logo" href="index.html">
+  
+  
+  
+  
+    
+    
+    
+    <img src="_static/moabb_logo.svg" class="logo__image only-light" alt="Logo image"/>
+    <script>document.write(`<img src="_static/moabb_logo.svg" class="logo__image only-dark" alt="Logo image"/>`);</script>
+  
+  
+</a></div>
+    
+  </div>
+  
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="overview.html">
+                        Overview
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_tutorials/index.html">
+                        Getting Started
+                      </a>
+                    </li>
+                
+            <div class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
+                    More
+                </button>
+                <div class="dropdown-menu">
+                    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Gallery
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
+                        API reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        Release notes
+                      </a>
+                    </li>
+                
+                </div>
+            </div>
+            
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+  </button>
+`);
+</script>
+        </div>
+      
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span>
+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+      
+    </div>
+    
+  </div>
+  
+  
+    <div class="navbar-persistent--mobile">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+  <section id="module-moabb.analysis">
+<span id="analysis"></span><h1>Analysis<a class="headerlink" href="#module-moabb.analysis" title="Permalink to this heading">#</a></h1>
+<section id="plotting">
+<h2>Plotting<a class="headerlink" href="#plotting" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.score_plot</span></code></a>(data[, pipelines, ...])</p></td>
+<td><p>Plot scores for all pipelines and all datasets</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot" title="moabb.analysis.plotting.paired_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.paired_plot</span></code></a>(data, alg1, alg2)</p></td>
+<td><p>Generate a figure with a paired plot.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.summary_plot.html#moabb.analysis.plotting.summary_plot" title="moabb.analysis.plotting.summary_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.summary_plot</span></code></a>(sig_df, effect_df[, ...])</p></td>
+<td><p>Significance matrix to compare pipelines.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.meta_analysis_plot.html#moabb.analysis.plotting.meta_analysis_plot" title="moabb.analysis.plotting.meta_analysis_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.meta_analysis_plot</span></code></a>(stats_df, alg1, alg2)</p></td>
+<td><p>Meta-analysis to compare two algorithms across several datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="statistics">
+<h2>Statistics<a class="headerlink" href="#statistics" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences" title="moabb.analysis.meta_analysis.find_significant_differences"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.find_significant_differences</span></code></a>(df)</p></td>
+<td><p>Compute differences between pipelines across datasets.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics" title="moabb.analysis.meta_analysis.compute_dataset_statistics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.compute_dataset_statistics</span></code></a>(df)</p></td>
+<td><p>Compute meta-analysis statistics from results dataframe.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.combine_effects.html#moabb.analysis.meta_analysis.combine_effects" title="moabb.analysis.meta_analysis.combine_effects"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.combine_effects</span></code></a>(effects, nsubs)</p></td>
+<td><p>Combine effects for meta-analysis statistics.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.combine_pvalues.html#moabb.analysis.meta_analysis.combine_pvalues" title="moabb.analysis.meta_analysis.combine_pvalues"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.combine_pvalues</span></code></a>(p, nsubs)</p></td>
+<td><p>Combine p-values for meta-analysis statistics.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.collapse_session_scores.html#moabb.analysis.meta_analysis.collapse_session_scores" title="moabb.analysis.meta_analysis.collapse_session_scores"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.collapse_session_scores</span></code></a>(df)</p></td>
+<td><p>Prepare results dataframe for computing statistics.</p></td>
+</tr>
+</tbody>
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+</section>
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+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.benchmark.html">moabb.benchmark</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
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+  <section id="api-reference">
+<h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">#</a></h1>
+<section id="module-moabb.datasets">
+<span id="datasets"></span><h2>Datasets<a class="headerlink" href="#module-moabb.datasets" title="Permalink to this heading">#</a></h2>
+<p>A dataset handle and abstract low level access to the data. the dataset will
+takes data stored locally, in the format in which they have been downloaded,
+and will convert them into a MNE raw object. There are options to pool all the
+different recording sessions per subject or to evaluate them separately.</p>
+<p>See <a class="github reference external" href="https://github.com/NeuroTechX/moabb/wiki/Datasets-Support">NeuroTechX/moabb</a> for detail
+on datasets (electrodes, number of trials, sessions, etc.)</p>
+<section id="motor-imagery-datasets">
+<h3>Motor Imagery Datasets<a class="headerlink" href="#motor-imagery-datasets" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AlexMI</span></code></a>()</p></td>
+<td><p>Alex Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></a>()</p></td>
+<td><p>BNCI 2014-001 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_002.html#moabb.datasets.BNCI2014_002" title="moabb.datasets.BNCI2014_002"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_002</span></code></a>()</p></td>
+<td><p>BNCI 2014-002 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_004</span></code></a>()</p></td>
+<td><p>BNCI 2014-004 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_001.html#moabb.datasets.BNCI2015_001" title="moabb.datasets.BNCI2015_001"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2015_001</span></code></a>()</p></td>
+<td><p>BNCI 2015-001 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_004.html#moabb.datasets.BNCI2015_004" title="moabb.datasets.BNCI2015_004"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2015_004</span></code></a>()</p></td>
+<td><p>BNCI 2015-004 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017" title="moabb.datasets.Cho2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cho2017</span></code></a>()</p></td>
+<td><p>Motor Imagery dataset from Cho et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_MI.html#moabb.datasets.Lee2019_MI" title="moabb.datasets.Lee2019_MI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lee2019_MI</span></code></a>([train_run, test_run, ...])</p></td>
+<td><p>BMI/OpenBMI dataset for MI.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.GrosseWentrup2009.html#moabb.datasets.GrosseWentrup2009" title="moabb.datasets.GrosseWentrup2009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GrosseWentrup2009</span></code></a>()</p></td>
+<td><p>Munich Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Ofner2017.html#moabb.datasets.Ofner2017" title="moabb.datasets.Ofner2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ofner2017</span></code></a>([imagined, executed])</p></td>
+<td><p>Motor Imagery ataset from Ofner et al 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a>([imagined, executed])</p></td>
+<td><p>Physionet Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017" title="moabb.datasets.Schirrmeister2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a>()</p></td>
+<td><p>High-gamma dataset described in Schirrmeister et al. 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Shin2017A.html#moabb.datasets.Shin2017A" title="moabb.datasets.Shin2017A"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Shin2017A</span></code></a>([accept])</p></td>
+<td><p>Motor Imagey Dataset from Shin et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Shin2017B.html#moabb.datasets.Shin2017B" title="moabb.datasets.Shin2017B"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Shin2017B</span></code></a>([accept])</p></td>
+<td><p>Mental Arithmetic Dataset from Shin et al 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014" title="moabb.datasets.Weibo2014"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Weibo2014</span></code></a>()</p></td>
+<td><p>Motor Imagery dataset from Weibo et al 2014.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Zhou2016</span></code></a>()</p></td>
+<td><p>Motor Imagery dataset from Zhou et al 2016.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="erp-datasets">
+<h3>ERP Datasets<a class="headerlink" href="#erp-datasets" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2012.html#moabb.datasets.BI2012" title="moabb.datasets.BI2012"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2012</span></code></a>([Training, Online])</p></td>
+<td><p>P300 dataset BI2012 from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2013a.html#moabb.datasets.BI2013a" title="moabb.datasets.BI2013a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2013a</span></code></a>([NonAdaptive, Adaptive, Training, ...])</p></td>
+<td><p>P300 dataset BI2013a from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a" title="moabb.datasets.BI2014a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014a</span></code></a>()</p></td>
+<td><p>P300 dataset BI2014a from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2014b.html#moabb.datasets.BI2014b" title="moabb.datasets.BI2014b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014b</span></code></a>()</p></td>
+<td><p>P300 dataset BI2014b from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2015a.html#moabb.datasets.BI2015a" title="moabb.datasets.BI2015a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015a</span></code></a>()</p></td>
+<td><p>P300 dataset BI2015a from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2015b.html#moabb.datasets.BI2015b" title="moabb.datasets.BI2015b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015b</span></code></a>()</p></td>
+<td><p>P300 dataset BI2015b from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_VR</span></code></a>([virtual_reality, screen_display])</p></td>
+<td><p>Dataset of an EEG-based BCI experiment in Virtual Reality using P300.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_008.html#moabb.datasets.BNCI2014_008" title="moabb.datasets.BNCI2014_008"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_008</span></code></a>()</p></td>
+<td><p>BNCI 2014-008 P300 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_009</span></code></a>()</p></td>
+<td><p>BNCI 2014-009 P300 dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_003.html#moabb.datasets.BNCI2015_003" title="moabb.datasets.BNCI2015_003"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2015_003</span></code></a>()</p></td>
+<td><p>BNCI 2015-003 P300 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.DemonsP300.html#moabb.datasets.DemonsP300" title="moabb.datasets.DemonsP300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DemonsP300</span></code></a>()</p></td>
+<td><p>Visual P300 dataset recorded in Virtual Reality (VR) game Raccoons versus Demons.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.EPFLP300.html#moabb.datasets.EPFLP300" title="moabb.datasets.EPFLP300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EPFLP300</span></code></a>()</p></td>
+<td><p>P300 dataset from Hoffmann et al 2008.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Huebner2017.html#moabb.datasets.Huebner2017" title="moabb.datasets.Huebner2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Huebner2017</span></code></a>([interval, raw_slice_offset, ...])</p></td>
+<td><p>Learning from label proportions for a visual matrix speller (ERP) dataset from Hübner et al 2017 <a class="reference internal" href="generated/moabb.datasets.Huebner2017.html#r0a211c89d39d-1" id="id1"><span>[R0a211c89d39d-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Huebner2018.html#moabb.datasets.Huebner2018" title="moabb.datasets.Huebner2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Huebner2018</span></code></a>([interval, raw_slice_offset, ...])</p></td>
+<td><p>Mixture of LLP and EM for a visual matrix speller (ERP) dataset from Hübner et al 2018 <a class="reference internal" href="generated/moabb.datasets.Huebner2018.html#r8f30fc0d0ace-1" id="id2"><span>[R8f30fc0d0ace-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_ERP.html#moabb.datasets.Lee2019_ERP" title="moabb.datasets.Lee2019_ERP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lee2019_ERP</span></code></a>([train_run, test_run, ...])</p></td>
+<td><p>BMI/OpenBMI dataset for P300.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019" title="moabb.datasets.Sosulski2019"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sosulski2019</span></code></a>([use_soas_as_sessions, ...])</p></td>
+<td><p>P300 dataset from initial spot study.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="ssvep-datasets">
+<h3>SSVEP Datasets<a class="headerlink" href="#ssvep-datasets" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Kalunga2016</span></code></a>()</p></td>
+<td><p>SSVEP Exo dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Nakanishi2015.html#moabb.datasets.Nakanishi2015" title="moabb.datasets.Nakanishi2015"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Nakanishi2015</span></code></a>()</p></td>
+<td><p>SSVEP Nakanishi 2015 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Wang2016.html#moabb.datasets.Wang2016" title="moabb.datasets.Wang2016"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Wang2016</span></code></a>()</p></td>
+<td><p>SSVEP Wang 2016 dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM1.html#moabb.datasets.MAMEM1" title="moabb.datasets.MAMEM1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAMEM1</span></code></a>()</p></td>
+<td><p>SSVEP MAMEM 1 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM2.html#moabb.datasets.MAMEM2" title="moabb.datasets.MAMEM2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAMEM2</span></code></a>()</p></td>
+<td><p>SSVEP MAMEM 2 dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAMEM3</span></code></a>()</p></td>
+<td><p>SSVEP MAMEM 3 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_SSVEP.html#moabb.datasets.Lee2019_SSVEP" title="moabb.datasets.Lee2019_SSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lee2019_SSVEP</span></code></a>([train_run, test_run, ...])</p></td>
+<td><p>BMI/OpenBMI dataset for SSVEP.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="c-vep-datasets">
+<h3>c-VEP Datasets<a class="headerlink" href="#c-vep-datasets" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Thielen2015.html#moabb.datasets.Thielen2015" title="moabb.datasets.Thielen2015"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Thielen2015</span></code></a>()</p></td>
+<td><p>c-VEP dataset from Thielen et al. (2015).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Thielen2021.html#moabb.datasets.Thielen2021" title="moabb.datasets.Thielen2021"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Thielen2021</span></code></a>()</p></td>
+<td><p>c-VEP dataset from Thielen et al. (2021).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosBurstVEP40.html#moabb.datasets.CastillosBurstVEP40" title="moabb.datasets.CastillosBurstVEP40"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosBurstVEP40</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosBurstVEP100.html#moabb.datasets.CastillosBurstVEP100" title="moabb.datasets.CastillosBurstVEP100"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosBurstVEP100</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosCVEP40.html#moabb.datasets.CastillosCVEP40" title="moabb.datasets.CastillosCVEP40"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosCVEP40</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosCVEP100.html#moabb.datasets.CastillosCVEP100" title="moabb.datasets.CastillosCVEP100"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosCVEP100</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="resting-state-datasets">
+<h3>Resting State Datasets<a class="headerlink" href="#resting-state-datasets" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_PHMD</span></code></a>()</p></td>
+<td><p>Passive Head Mounted Display with Music Listening dataset.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="base-utils">
+<h3>Base &amp; Utils<a class="headerlink" href="#base-utils" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseDataset</span></code></a>(subjects, ...[, doi, ...])</p></td>
+<td><p>Abstract Moabb BaseDataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.CacheConfig</span></code></a>([save_raw, save_epochs, ...])</p></td>
+<td><p>Configuration for caching of datasets.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.fake.FakeDataset.html#moabb.datasets.fake.FakeDataset" title="moabb.datasets.fake.FakeDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fake.FakeDataset</span></code></a>([event_list, n_sessions, ...])</p></td>
+<td><p>Fake Dataset for test purpose.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.fake.FakeVirtualRealityDataset.html#moabb.datasets.fake.FakeVirtualRealityDataset" title="moabb.datasets.fake.FakeVirtualRealityDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fake.FakeVirtualRealityDataset</span></code></a>([seed])</p></td>
+<td><p>Fake Cattan2019_VR dataset for test purpose.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.data_path.html#moabb.datasets.download.data_path" title="moabb.datasets.download.data_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.data_path</span></code></a>(url, sign[, path, ...])</p></td>
+<td><p>Get path to local copy of given dataset URL.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.download.data_dl.html#moabb.datasets.download.data_dl" title="moabb.datasets.download.data_dl"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.data_dl</span></code></a>(url, sign[, path, ...])</p></td>
+<td><p>Download file from url to specified path.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_issue_request.html#moabb.datasets.download.fs_issue_request" title="moabb.datasets.download.fs_issue_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_issue_request</span></code></a>(method, url, headers)</p></td>
+<td><p>Wrapper for HTTP request.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_list.html#moabb.datasets.download.fs_get_file_list" title="moabb.datasets.download.fs_get_file_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_list</span></code></a>(article_id[, version])</p></td>
+<td><p>List all the files associated with a given article.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_hash.html#moabb.datasets.download.fs_get_file_hash" title="moabb.datasets.download.fs_get_file_hash"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_hash</span></code></a>(filelist)</p></td>
+<td><p>Returns a dict associating figshare file id to MD5 hash.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_id.html#moabb.datasets.download.fs_get_file_id" title="moabb.datasets.download.fs_get_file_id"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_id</span></code></a>(filelist)</p></td>
+<td><p>Returns a dict associating filename to figshare file id.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_name.html#moabb.datasets.download.fs_get_file_name" title="moabb.datasets.download.fs_get_file_name"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_name</span></code></a>(filelist)</p></td>
+<td><p>Returns a dict associating figshare file id to filename.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.utils.dataset_search.html#moabb.datasets.utils.dataset_search" title="moabb.datasets.utils.dataset_search"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.dataset_search</span></code></a>([paradigm, ...])</p></td>
+<td><p>Returns a list of datasets that match a given criteria.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.utils.find_intersecting_channels.html#moabb.datasets.utils.find_intersecting_channels" title="moabb.datasets.utils.find_intersecting_channels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.find_intersecting_channels</span></code></a>(datasets[, ...])</p></td>
+<td><p>Given a list of dataset instances return a list of channels shared by all datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb.datasets.compound_dataset">
+<span id="compound-datasets"></span><h2>Compound Datasets<a class="headerlink" href="#module-moabb.datasets.compound_dataset" title="Permalink to this heading">#</a></h2>
+<section id="id3">
+<h3>ERP Datasets<a class="headerlink" href="#id3" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014a_Il.html#moabb.datasets.compound_dataset.BI2014a_Il" title="moabb.datasets.compound_dataset.BI2014a_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014a_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2014a with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014b_Il.html#moabb.datasets.compound_dataset.BI2014b_Il" title="moabb.datasets.compound_dataset.BI2014b_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014b_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2014b with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015a_Il.html#moabb.datasets.compound_dataset.BI2015a_Il" title="moabb.datasets.compound_dataset.BI2015a_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015a_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2015a with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015b_Il.html#moabb.datasets.compound_dataset.BI2015b_Il" title="moabb.datasets.compound_dataset.BI2015b_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015b_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2015b with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html#moabb.datasets.compound_dataset.Cattan2019_VR_Il" title="moabb.datasets.compound_dataset.Cattan2019_VR_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_VR_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from Cattan2019_VR with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI_Il.html#moabb.datasets.compound_dataset.BI_Il" title="moabb.datasets.compound_dataset.BI_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI_Il</span></code></a>()</p></td>
+<td><p>Subjects from braininvaders datasets with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb.evaluations">
+<span id="evaluations"></span><h2>Evaluations<a class="headerlink" href="#module-moabb.evaluations" title="Permalink to this heading">#</a></h2>
+<p>An evaluation defines how we go from trials per subject and session to a
+generalization statistic (AUC score, f-score, accuracy, etc) – it can be
+either within-recording-session accuracy, across-session within-subject
+accuracy, across-subject accuracy, or other transfer learning settings.</p>
+<section id="id4">
+<h3>Evaluations<a class="headerlink" href="#id4" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WithinSessionEvaluation</span></code></a>([n_perms, data_size])</p></td>
+<td><p>Performance evaluation within session (k-fold cross-validation)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossSessionEvaluation</span></code></a>(paradigm[, datasets, ...])</p></td>
+<td><p>Cross-session performance evaluation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossSubjectEvaluation</span></code></a>(paradigm[, datasets, ...])</p></td>
+<td><p>Cross-subject evaluation performance.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id5">
+<h3>Base &amp; Utils<a class="headerlink" href="#id5" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation" title="moabb.evaluations.base.BaseEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseEvaluation</span></code></a>(paradigm[, datasets, ...])</p></td>
+<td><p>Base class that defines necessary operations for an evaluation.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb.paradigms">
+<span id="paradigms"></span><h2>Paradigms<a class="headerlink" href="#module-moabb.paradigms" title="Permalink to this heading">#</a></h2>
+<p>A paradigm defines how the raw data will be converted to trials ready to be
+processed by a decoding algorithm.</p>
+<p>This is a function of the paradigm used, i.e. in motor imagery one can
+have two-class, multi-class, or continuous paradigms; similarly,
+different preprocessing is necessary for ERP vs ERD paradigms.</p>
+<section id="motor-imagery-paradigms">
+<h3>Motor Imagery Paradigms<a class="headerlink" href="#motor-imagery-paradigms" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MotorImagery</span></code></a>([n_classes])</p></td>
+<td><p>N-class motor imagery.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LeftRightImagery</span></code></a>(**kwargs)</p></td>
+<td><p>Motor Imagery for left hand/right hand classification.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankLeftRightImagery</span></code></a>(**kwargs)</p></td>
+<td><p>Filter Bank Motor Imagery for left hand/right hand classification.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery" title="moabb.paradigms.FilterBankMotorImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankMotorImagery</span></code></a>([n_classes])</p></td>
+<td><p>Filter bank n-class motor imagery.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="p300-paradigms">
+<h3>P300 Paradigms<a class="headerlink" href="#p300-paradigms" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.SinglePass.html#moabb.paradigms.SinglePass" title="moabb.paradigms.SinglePass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SinglePass</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single Bandpass filter P300.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">P300</span></code></a>(**kwargs)</p></td>
+<td><p>P300 for Target/NonTarget classification.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="ssvep-paradigms">
+<h3>SSVEP Paradigms<a class="headerlink" href="#ssvep-paradigms" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SSVEP</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single bandpass filter SSVEP.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP" title="moabb.paradigms.FilterBankSSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankSSVEP</span></code></a>([filters])</p></td>
+<td><p>Filtered bank n-class SSVEP paradigm.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="c-vep-paradigms">
+<h3>c-VEP Paradigms<a class="headerlink" href="#c-vep-paradigms" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.CVEP.html#moabb.paradigms.CVEP" title="moabb.paradigms.CVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CVEP</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single bandpass c-VEP paradigm for epoch-level decoding.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankCVEP.html#moabb.paradigms.FilterBankCVEP" title="moabb.paradigms.FilterBankCVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankCVEP</span></code></a>([filters])</p></td>
+<td><p>Filterbank c-VEP paradigm for epoch-level decoding.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="fixed-interval-windows-processings">
+<h3>Fixed Interval Windows Processings<a class="headerlink" href="#fixed-interval-windows-processings" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FixedIntervalWindowsProcessing</span></code></a>([fmin, fmax, ...])</p></td>
+<td><p>Fixed interval windows processing.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html#moabb.paradigms.FilterBankFixedIntervalWindowsProcessing" title="moabb.paradigms.FilterBankFixedIntervalWindowsProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankFixedIntervalWindowsProcessing</span></code></a>([...])</p></td>
+<td><p>Filter bank fixed interval windows processing.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id6">
+<h3>Base &amp; Utils<a class="headerlink" href="#id6" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery" title="moabb.paradigms.motor_imagery.BaseMotorImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">motor_imagery.BaseMotorImagery</span></code></a>([filters, ...])</p></td>
+<td><p>Base Motor imagery paradigm.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.motor_imagery.SinglePass.html#moabb.paradigms.motor_imagery.SinglePass" title="moabb.paradigms.motor_imagery.SinglePass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">motor_imagery.SinglePass</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single Bandpass filter motor Imagery.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.motor_imagery.FilterBank.html#moabb.paradigms.motor_imagery.FilterBank" title="moabb.paradigms.motor_imagery.FilterBank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">motor_imagery.FilterBank</span></code></a>([filters])</p></td>
+<td><p>Filter Bank MI.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.p300.BaseP300.html#moabb.paradigms.p300.BaseP300" title="moabb.paradigms.p300.BaseP300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p300.BaseP300</span></code></a>([filters, events, tmin, tmax, ...])</p></td>
+<td><p>Base P300 paradigm.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP" title="moabb.paradigms.ssvep.BaseSSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ssvep.BaseSSVEP</span></code></a>([filters, events, ...])</p></td>
+<td><p>Base SSVEP Paradigm.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html#moabb.paradigms.BaseFixedIntervalWindowsProcessing" title="moabb.paradigms.BaseFixedIntervalWindowsProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseFixedIntervalWindowsProcessing</span></code></a>([...])</p></td>
+<td><p>Base class for fixed interval windows processing.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.base.BaseParadigm.html#moabb.paradigms.base.BaseParadigm" title="moabb.paradigms.base.BaseParadigm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseParadigm</span></code></a>(filters[, events, tmin, ...])</p></td>
+<td><p>Base class for paradigms.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing" title="moabb.paradigms.base.BaseProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseProcessing</span></code></a>(filters[, tmin, tmax, ...])</p></td>
+<td><p>Base Processing.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb.pipelines">
+<span id="pipelines"></span><h2>Pipelines<a class="headerlink" href="#module-moabb.pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipeline defines all steps required by an algorithm to obtain predictions.</p>
+<p>Pipelines are typically a chain of sklearn compatible transformers and
+end with a sklearn compatible estimator.</p>
+<section id="id7">
+<h3>Pipelines<a class="headerlink" href="#id7" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance" title="moabb.pipelines.features.LogVariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.LogVariance</span></code></a>()</p></td>
+<td><p>LogVariance transformer.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.FM.html#moabb.pipelines.features.FM" title="moabb.pipelines.features.FM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.FM</span></code></a>([freq])</p></td>
+<td><p>Transformer to scale sampling frequency.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal" title="moabb.pipelines.features.ExtendedSSVEPSignal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.ExtendedSSVEPSignal</span></code></a>()</p></td>
+<td><p>Prepare FilterBank SSVEP EEG signal for estimating extended covariances.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.AugmentedDataset.html#moabb.pipelines.features.AugmentedDataset" title="moabb.pipelines.features.AugmentedDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.AugmentedDataset</span></code></a>([order, lag])</p></td>
+<td><p>Dataset augmentation methods in a higher dimensional space.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.StandardScaler_Epoch.html#moabb.pipelines.features.StandardScaler_Epoch" title="moabb.pipelines.features.StandardScaler_Epoch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.StandardScaler_Epoch</span></code></a>()</p></td>
+<td><p>Function to standardize the X raw data for the DeepLearning Method.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.csp.TRCSP.html#moabb.pipelines.csp.TRCSP" title="moabb.pipelines.csp.TRCSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">csp.TRCSP</span></code></a>([nfilter, metric, log, alpha])</p></td>
+<td><p>Weighted Tikhonov-regularized CSP as described in Lotte and Guan 2011.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification.SSVEP_CCA</span></code></a>(interval, freqs[, ...])</p></td>
+<td><p>Classifier based on Canonical Correlation Analysis for SSVEP.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA" title="moabb.pipelines.classification.SSVEP_TRCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification.SSVEP_TRCA</span></code></a>(interval, freqs[, ...])</p></td>
+<td><p>Classifier based on the Task-Related Component Analysis method <a href="#id19"><span class="problematic" id="id8">[1]_</span></a> for SSVEP.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA" title="moabb.pipelines.classification.SSVEP_MsetCCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification.SSVEP_MsetCCA</span></code></a>(freqs[, ...])</p></td>
+<td><p>Classifier based on MsetCCA for SSVEP.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#moabb.pipelines.deep_learning.KerasDeepConvNet" title="moabb.pipelines.deep_learning.KerasDeepConvNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasDeepConvNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the Deep Convolutional Network as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#r679315cfbef6-1" id="id9"><span>[R679315cfbef6-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#moabb.pipelines.deep_learning.KerasEEGITNet" title="moabb.pipelines.deep_learning.KerasEEGITNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGITNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the EEGITNet as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#rf5b2ee1af1ae-1" id="id10"><span>[Rf5b2ee1af1ae-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#moabb.pipelines.deep_learning.KerasEEGNet_8_2" title="moabb.pipelines.deep_learning.KerasEEGNet_8_2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGNet_8_2</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the EEGNet as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#rd83becb56589-1" id="id11"><span>[Rd83becb56589-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#moabb.pipelines.deep_learning.KerasEEGNeX" title="moabb.pipelines.deep_learning.KerasEEGNeX"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGNeX</span></code></a>(loss[, optimizer, ...])</p></td>
+<td><p>Keras implementation of the EEGNex as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#r643fa75c3283-1" id="id12"><span>[R643fa75c3283-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#moabb.pipelines.deep_learning.KerasEEGTCNet" title="moabb.pipelines.deep_learning.KerasEEGTCNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGTCNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the EEGTCNet as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#r89b58824c471-1" id="id13"><span>[R89b58824c471-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#moabb.pipelines.deep_learning.KerasShallowConvNet" title="moabb.pipelines.deep_learning.KerasShallowConvNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasShallowConvNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the Shallow Convolutional Network as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#r2ccacb732305-1" id="id14"><span>[R2ccacb732305-1]</span></a>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id15">
+<h3>Base &amp; Utils<a class="headerlink" href="#id15" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils.create_pipeline_from_config.html#moabb.pipelines.utils.create_pipeline_from_config" title="moabb.pipelines.utils.create_pipeline_from_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.create_pipeline_from_config</span></code></a>(config)</p></td>
+<td><p>Create a pipeline from a config file.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils.FilterBank.html#moabb.pipelines.utils.FilterBank" title="moabb.pipelines.utils.FilterBank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.FilterBank</span></code></a>(estimator[, flatten])</p></td>
+<td><p>Apply a given identical pipeline over a bank of filter.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet.html#moabb.pipelines.utils_deep_model.EEGNet" title="moabb.pipelines.utils_deep_model.EEGNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_deep_model.EEGNet</span></code></a>(data, input_layer[, ...])</p></td>
+<td><p>EEGNet block implementation as described in <a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet.html#r820c2366bc63-1" id="id16"><span>[R820c2366bc63-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html#moabb.pipelines.utils_deep_model.EEGNet_TC" title="moabb.pipelines.utils_deep_model.EEGNet_TC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_deep_model.EEGNet_TC</span></code></a>(self, input_layer)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.TCN_block.html#moabb.pipelines.utils_deep_model.TCN_block" title="moabb.pipelines.utils_deep_model.TCN_block"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_deep_model.TCN_block</span></code></a>(input_layer, ...)</p></td>
+<td><p>Temporal Convolutional Network (TCN), TCN_block from <a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.TCN_block.html#r2eea69aed7b6-1" id="id17"><span>[R2eea69aed7b6-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html#moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader" title="moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_pytorch.BraindecodeDatasetLoader</span></code></a>([...])</p></td>
+<td><p>Class to Load the data from MOABB in a format compatible with braindecode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html#moabb.pipelines.utils_pytorch.InputShapeSetterEEG" title="moabb.pipelines.utils_pytorch.InputShapeSetterEEG"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_pytorch.InputShapeSetterEEG</span></code></a>([...])</p></td>
+<td><p>Sets the input dimension of the PyTorch module to the input dimension of the training data.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb.analysis">
+<span id="analysis"></span><h2>Analysis<a class="headerlink" href="#module-moabb.analysis" title="Permalink to this heading">#</a></h2>
+<section id="plotting">
+<h3>Plotting<a class="headerlink" href="#plotting" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.score_plot</span></code></a>(data[, pipelines, ...])</p></td>
+<td><p>Plot scores for all pipelines and all datasets</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot" title="moabb.analysis.plotting.paired_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.paired_plot</span></code></a>(data, alg1, alg2)</p></td>
+<td><p>Generate a figure with a paired plot.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.summary_plot.html#moabb.analysis.plotting.summary_plot" title="moabb.analysis.plotting.summary_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.summary_plot</span></code></a>(sig_df, effect_df[, ...])</p></td>
+<td><p>Significance matrix to compare pipelines.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.plotting.meta_analysis_plot.html#moabb.analysis.plotting.meta_analysis_plot" title="moabb.analysis.plotting.meta_analysis_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plotting.meta_analysis_plot</span></code></a>(stats_df, alg1, alg2)</p></td>
+<td><p>Meta-analysis to compare two algorithms across several datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="statistics">
+<h3>Statistics<a class="headerlink" href="#statistics" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences" title="moabb.analysis.meta_analysis.find_significant_differences"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.find_significant_differences</span></code></a>(df)</p></td>
+<td><p>Compute differences between pipelines across datasets.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics" title="moabb.analysis.meta_analysis.compute_dataset_statistics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.compute_dataset_statistics</span></code></a>(df)</p></td>
+<td><p>Compute meta-analysis statistics from results dataframe.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.combine_effects.html#moabb.analysis.meta_analysis.combine_effects" title="moabb.analysis.meta_analysis.combine_effects"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.combine_effects</span></code></a>(effects, nsubs)</p></td>
+<td><p>Combine effects for meta-analysis statistics.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.combine_pvalues.html#moabb.analysis.meta_analysis.combine_pvalues" title="moabb.analysis.meta_analysis.combine_pvalues"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.combine_pvalues</span></code></a>(p, nsubs)</p></td>
+<td><p>Combine p-values for meta-analysis statistics.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.analysis.meta_analysis.collapse_session_scores.html#moabb.analysis.meta_analysis.collapse_session_scores" title="moabb.analysis.meta_analysis.collapse_session_scores"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meta_analysis.collapse_session_scores</span></code></a>(df)</p></td>
+<td><p>Prepare results dataframe for computing statistics.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb">
+<span id="utils"></span><h2>Utils<a class="headerlink" href="#module-moabb" title="Permalink to this heading">#</a></h2>
+<section id="benchmark">
+<h3>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark"><code class="xref py py-obj docutils literal notranslate"><span class="pre">benchmark</span></code></a>([pipelines, evaluations, ...])</p></td>
+<td><p>Run benchmarks for selected pipelines and datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id18">
+<h3>Utils<a class="headerlink" href="#id18" title="Permalink to this heading">#</a></h3>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_log_level</span></code></a>([level])</p></td>
+<td><p>Set log level.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setup_seed</span></code></a>(seed)</p></td>
+<td><p>Set the seed for random, numpy, TensorFlow and PyTorch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.set_download_dir.html#moabb.set_download_dir" title="moabb.set_download_dir"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_download_dir</span></code></a>(path)</p></td>
+<td><p>Set the download directory if required to change from default mne path.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.make_process_pipelines.html#moabb.make_process_pipelines" title="moabb.make_process_pipelines"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_process_pipelines</span></code></a>(processing, dataset)</p></td>
+<td><p>Shortcut for the method <a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.make_process_pipelines" title="moabb.paradigms.base.BaseProcessing.make_process_pipelines"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseProcessing.make_process_pipelines()</span></code></a></p></td>
+</tr>
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+<li class="toctree-l2"><a class="reference internal" href="../../auto_tutorials/4_adding_a_dataset.html">Tutorial 4: Creating a dataset class</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html">Tutorial 5: Creating a dataset class</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../auto_tutorials/plot_Getting_Started.html">Tutorial 0: Getting Started</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../auto_tutorials/tutorial_1_simple_example_motor_imagery.html">Tutorial 1: Simple Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../auto_tutorials/tutorial_2_using_mulitple_datasets.html">Tutorial 2: Using multiple datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html">Tutorial 3: Benchmarking multiple pipelines</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../example_codecarbon.html">Benchmarking with MOABB showing the CO2 footprint</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../load_model.html">Load Model (Scikit, Pytorch, Keras) with MOABB</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../noplot_bids_conversion.html">Convert a MOABB dataset to BIDS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../noplot_phmd_ml_spectrum.html">Spectral analysis of the trials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../noplot_vr_pc_p300_different_epoch_size.html">Changing epoch size in P300 VR dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_benchmark.html">Examples of how to use MOABB to benchmark pipelines.</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../plot_benchmark_DL.html">Benchmarking on MOABB with Tensorflow deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_benchmark_braindecode.html">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_benchmark_grid_search.html">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_cross_subject_ssvep.html">Cross-Subject SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_disk_cache.html">Cache on disk intermediate data processing states</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_explore_paradigm.html">Explore Paradigm Object</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_fixed_interval_windows.html">Fixed interval windows processing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_within_session_p300.html">Within Session P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../plot_within_session_ssvep.html">Within Session SSVEP</a></li>
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+  <section id="advanced-examples">
+<span id="sphx-glr-auto-examples-advanced-examples"></span><h1>Advanced examples<a class="headerlink" href="#advanced-examples" title="Permalink to this heading">#</a></h1>
+<p>These examples show various advanced topics:</p>
+<ul class="simple">
+<li><p>using scikit-learn pipeline with MNE inputs</p></li>
+<li><p>selecting electrodes or resampling signal</p></li>
+<li><p>using filterbank approach in motor imagery</p></li>
+<li><p>apply statistics for meta-analysis</p></li>
+<li><p>using a gridsearch in within-subject decoding</p></li>
+</ul>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
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+</nav></div>
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+<div class="header-article-items header-article__inner">
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+      <a href="../../index.html" class="nav-link" aria-label="Home">
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Advanced examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">FilterBank CSP versus CSP</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
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+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="filterbank-csp-versus-csp">
+<span id="sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"></span><h1>FilterBank CSP versus CSP<a class="headerlink" href="#filterbank-csp-versus-csp" title="Permalink to this heading">#</a></h1>
+<p>This example show a comparison of CSP versus FilterBank CSP on the
+very popular dataset 2a from the BCI competition IV.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Alexandre Barachant &lt;alexandre.barachant@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FilterBankLeftRightImagery</span></a><span class="p">,</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+<span class="kn">from</span> <span class="nn">moabb.pipelines.utils</span> <span class="kn">import</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.utils.FilterBank.html#moabb.pipelines.utils.FilterBank" title="moabb.pipelines.utils.FilterBank" class="sphx-glr-backref-module-moabb-pipelines-utils sphx-glr-backref-type-py-function"><span class="n">FilterBank</span></a>
+
+
+<a href="../../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>The CSP implementation from MNE is used. We selected 8 CSP components, as
+usually done in the literature.</p>
+<p>The second pipeline is the filter bank CSP. We use the FilterBank object
+with a CSP estimator. We set up the CSP to 4 components, to compensate for
+the higher dimensionality.</p>
+<p>The two pipelines will be applied on two different paradigm, so they have
+their own dict.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_fb</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_fb</span></a><span class="p">[</span><span class="s2">&quot;FBCSP+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.utils.FilterBank.html#moabb.pipelines.utils.FilterBank" title="moabb.pipelines.utils.FilterBank" class="sphx-glr-backref-module-moabb-pipelines-utils sphx-glr-backref-type-py-function"><span class="n">FilterBank</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">4</span><span class="p">)),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>Since two different preprocessing will be applied, we have two different
+paradigm objects. We have to make sure their filter matches so the comparison
+will be fair.</p>
+<p>The first one is a standard <cite>LeftRightImagery</cite> with a 8 to 35 Hz broadband
+filter.</p>
+<p>The second is a <cite>FilterBankLeftRightImagery</cite> with a bank of 2 filters, ranging
+from 8 to 35 Hz.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Because this is being auto-generated we only use 2 subjects</span>
+<a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+
+<span class="c1"># broadband filters</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a> <span class="o">=</span> <span class="mi">8</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a> <span class="o">=</span> <span class="mi">35</span>
+<a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="p">)</span>
+<a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+
+<span class="c1"># Bank of 2 filters</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">filters</span></a> <span class="o">=</span> <span class="p">[[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">24</span><span class="p">],</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">]]</span>
+<a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FilterBankLeftRightImagery</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">filters</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">filters</span></a><span class="p">)</span>
+<a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results_fb</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_fb</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]Subject 1 already processed
+
+BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]
+
+BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]Subject 1 already processed
+
+BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]
+</pre></div>
+</div>
+<p>After processing the two, we simply concatenate the results.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">results</span><span class="p">,</span> <span class="n">results_fb</span><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results via seaborn. We first display a pointplot
+with the average performance of each pipeline across session and subjects.
+The second plot is a paired scatter plot. Each point representing the score
+of a single session. An algorithm will outperform another is most of the
+points are in its quadrant.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">stripplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+    <span class="n">jitter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># paired plot</span>
+<span class="n">paired</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span>
+    <span class="n">values</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;session&quot;</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">paired</span> <span class="o">=</span> <span class="n">paired</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">paired</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;FBCSP+LDA&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">fit_reg</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_filterbank_csp_vs_csp_001.png" srcset="../../_images/sphx_glr_plot_filterbank_csp_vs_csp_001.png" alt="plot filterbank csp vs csp" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.261 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  9 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/4c45605b7f9e2012cf86c10fa0f2ed72/plot_filterbank_csp_vs_csp.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_filterbank_csp_vs_csp.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/cd325bfe64e15ce23db3b200e83ba9a2/plot_filterbank_csp_vs_csp.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_filterbank_csp_vs_csp.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
+  
+    <div class="footer-article-item"><!-- Previous / next buttons -->
+<div class="prev-next-area">
+    <a class="left-prev"
+       href="index.html"
+       title="previous page">
+      <i class="fa-solid fa-angle-left"></i>
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Advanced examples</p>
+      </div>
+    </a>
+    <a class="right-next"
+       href="plot_grid_search_withinsession.html"
+       title="next page">
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">GridSearch within a session</p>
+      </div>
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipelines">Create Pipelines</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot Results</a></li>
+</ul>
+  </nav></div>
+
+</div></div>
+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
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+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
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new file mode 100644
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@@ -0,0 +1,993 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../../_static/moabb_logo.png" type="image/png">
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+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.CastillosBurstVEP100.html">moabb.datasets.CastillosBurstVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.base.BaseDataset.html">moabb.datasets.base.BaseDataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
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+</nav></div>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Advanced examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">GridSearch within a session</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
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+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="gridsearch-within-a-session">
+<span id="sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"></span><h1>GridSearch within a session<a class="headerlink" href="#gridsearch-within-a-session" title="Permalink to this heading">#</a></h1>
+<p>This example demonstrates how to make a model selection in pipelines
+for finding the best model parameter, using grid search. Two models
+are compared, one “vanilla” model with model tuned via grid search.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">pickle</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/pickle.html#pickle.load" title="pickle.load" class="sphx-glr-backref-module-pickle sphx-glr-backref-type-py-function"><span class="n">load</span></a>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class"><span class="n">Pipeline</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a>
+
+
+<span class="c1"># Initialize parameter for the Band Pass filter</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a> <span class="o">=</span> <span class="mi">8</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a> <span class="o">=</span> <span class="mi">35</span>
+<span class="n">tmax</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="c1"># Select the Subject</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="c1"># Load the dataset</span>
+<a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;right_hand&quot;</span><span class="p">,</span> <span class="s2">&quot;left_hand&quot;</span><span class="p">]</span>
+
+<a href="../../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="n">tmax</span>
+<span class="p">)</span>
+
+<span class="c1"># Create a path and folder for every subject</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">path</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/os.path.html#os.path.join" title="os.path.join" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span></a><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="s2">&quot;Results&quot;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/os.html#os.makedirs" title="os.makedirs" class="sphx-glr-backref-module-os sphx-glr-backref-type-py-function"><span class="n">os</span><span class="o">.</span><span class="n">makedirs</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">path</span></a><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-the-pipelines">
+<h2>Create the Pipelines<a class="headerlink" href="#create-the-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Two pipelines implementing elastic net classifiers, one using a fixed
+l1_ratio (“VanillaEN”) and the other using a range of values to select
+l1_ratio (“GridSearchEN”)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;VanillaEN&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class"><span class="n">Pipeline</span></a><span class="p">(</span>
+    <span class="n">steps</span><span class="o">=</span><span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;Covariances&quot;</span><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(</span><span class="s2">&quot;cov&quot;</span><span class="p">)),</span>
+        <span class="p">(</span><span class="s2">&quot;Tangent_Space&quot;</span><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">)),</span>
+        <span class="p">(</span>
+            <span class="s2">&quot;LogistReg&quot;</span><span class="p">,</span>
+            <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span>
+                <span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;elasticnet&quot;</span><span class="p">,</span>
+                <span class="n">l1_ratio</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span>
+                <span class="n">intercept_scaling</span><span class="o">=</span><span class="mf">1000.0</span><span class="p">,</span>
+                <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;saga&quot;</span><span class="p">,</span>
+                <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">),</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;GridSearchEN&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class"><span class="n">Pipeline</span></a><span class="p">(</span>
+    <span class="n">steps</span><span class="o">=</span><span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;Covariances&quot;</span><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(</span><span class="s2">&quot;cov&quot;</span><span class="p">)),</span>
+        <span class="p">(</span><span class="s2">&quot;Tangent_Space&quot;</span><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">)),</span>
+        <span class="p">(</span>
+            <span class="s2">&quot;LogistReg&quot;</span><span class="p">,</span>
+            <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span>
+                <span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;elasticnet&quot;</span><span class="p">,</span>
+                <span class="n">l1_ratio</span><span class="o">=</span><span class="mf">0.70</span><span class="p">,</span>
+                <span class="n">intercept_scaling</span><span class="o">=</span><span class="mf">1000.0</span><span class="p">,</span>
+                <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;saga&quot;</span><span class="p">,</span>
+                <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">),</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>The search space for parameters is defined as a dictionary, specifying the
+name of the estimator and the parameter name as a key.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">[</span><span class="s2">&quot;GridSearchEN&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;LogistReg__l1_ratio&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.30</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">,</span> <span class="mf">0.60</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">],</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+<section id="running-the-evaluation">
+<h2>Running the Evaluation<a class="headerlink" href="#running-the-evaluation" title="Permalink to this heading">#</a></h2>
+<p>If a param_grid is specified during process, the specified pipelines will
+automatically be run with a grid search.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <span class="n">datasets</span><span class="o">=</span><a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
+    <span class="n">hdf5_path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">path</span></a><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">save_model</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">result</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-WithinSession:   0%|          | 0/1 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-WithinSession: 100%|##########| 1/1 [00:10&lt;00:00, 10.98s/it]
+BNCI2014-001-WithinSession: 100%|##########| 1/1 [00:10&lt;00:00, 10.98s/it]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>The grid search allows to find better parameter during the
+evaluation, leading to better accuracy results.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">stripplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">result</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">,</span>
+    <span class="n">jitter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">result</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_grid_search_withinsession_001.png" srcset="../../_images/sphx_glr_plot_grid_search_withinsession_001.png" alt="plot grid search withinsession" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(44.972222222222214, 0.5, &#39;ROC AUC&#39;)
+</pre></div>
+</div>
+</section>
+<section id="load-best-model-parameter">
+<h2>Load Best Model Parameter<a class="headerlink" href="#load-best-model-parameter" title="Permalink to this heading">#</a></h2>
+<p>The best model are automatically saved in a pickle file, in the
+results directory. It is possible to load those model for each
+dataset, subject and session. Here, we could see that the grid
+search found a l1_ratio that is different from the baseline
+value.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span>
+    <span class="s2">&quot;./Results/Models_WithinSession/BNCI2014-001/1/1test/GridSearchEN/fitted_model_best.pkl&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;rb&quot;</span><span class="p">,</span>
+<span class="p">)</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/io.html#io.BufferedReader" title="io.BufferedReader" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pickle_file</span></a><span class="p">:</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchEN_Session_E</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pickle.html#pickle.load" title="pickle.load" class="sphx-glr-backref-module-pickle sphx-glr-backref-type-py-function"><span class="n">load</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/io.html#io.BufferedReader" title="io.BufferedReader" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pickle_file</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Best Parameter l1_ratio Session_E GridSearchEN &quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchEN_Session_E</span><span class="o">.</span><span class="n">best_params_</span></a><span class="p">[</span><span class="s2">&quot;LogistReg__l1_ratio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Best Parameter l1_ratio Session_E VanillaEN: &quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;VanillaEN&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">steps</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">l1_ratio</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span>
+    <span class="s2">&quot;./Results/Models_WithinSession/BNCI2014-001/1/0train/GridSearchEN/fitted_model_best.pkl&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;rb&quot;</span><span class="p">,</span>
+<span class="p">)</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/io.html#io.BufferedReader" title="io.BufferedReader" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pickle_file</span></a><span class="p">:</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchEN_Session_T</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pickle.html#pickle.load" title="pickle.load" class="sphx-glr-backref-module-pickle sphx-glr-backref-type-py-function"><span class="n">load</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/io.html#io.BufferedReader" title="io.BufferedReader" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pickle_file</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Best Parameter l1_ratio Session_T GridSearchEN &quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchEN_Session_T</span><span class="o">.</span><span class="n">best_params_</span></a><span class="p">[</span><span class="s2">&quot;LogistReg__l1_ratio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Best Parameter l1_ratio Session_T VanillaEN: &quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;VanillaEN&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">steps</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">l1_ratio</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best Parameter l1_ratio Session_E GridSearchEN  0.15
+Best Parameter l1_ratio Session_E VanillaEN:  0.75
+Best Parameter l1_ratio Session_T GridSearchEN  0.15
+Best Parameter l1_ratio Session_T VanillaEN:  0.75
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  13.381 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  257 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-examples-plot-grid-search-withinsession-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/b298e4b36adf2bb5cb6f65682eec852f/plot_grid_search_withinsession.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_grid_search_withinsession.py</span></code></a></p>
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+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/c0d5130414c37580257e7e0b7de777e2/plot_grid_search_withinsession.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_grid_search_withinsession.ipynb</span></code></a></p>
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+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#running-the-evaluation">Running the Evaluation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot Results</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="mne-epochs-based-pipelines">
+<span id="sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"></span><h1>MNE Epochs-based pipelines<a class="headerlink" href="#mne-epochs-based-pipelines" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use machine learning pipeline based on MNE Epochs
+instead of Numpy arrays. This is useful to make the most of the MNE code base
+and to embed EEG specific code inside sklearn pipelines.</p>
+<p>We will compare different pipelines for P300:
+- Logistic regression, based on MNE Epochs
+- XDAWN and Logistic Regression (LR), based on MNE Epochs
+- XDAWN extended covariance and LR on tangent space, based on Numpy</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Sylvain Chevallier</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+<span class="c1"># sphinx_gallery_thumbnail_number = 2</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.Vectorizer.html#mne.decoding.Vectorizer" title="mne.decoding.Vectorizer" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">Vectorizer</span></a>
+<span class="kn">from</span> <span class="nn">mne.preprocessing</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.preprocessing.Xdawn.html#mne.preprocessing.Xdawn" title="mne.preprocessing.Xdawn" class="sphx-glr-backref-module-mne-preprocessing sphx-glr-backref-type-py-class"><span class="n">Xdawn</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">BaseEstimator</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.TransformerMixin.html#sklearn.base.TransformerMixin" title="sklearn.base.TransformerMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TransformerMixin</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.analysis.meta_analysis</span> <span class="kn">import</span> <span class="p">(</span>  <span class="c1"># noqa: E501</span>
+    <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics" title="moabb.analysis.meta_analysis.compute_dataset_statistics" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">compute_dataset_statistics</span></a><span class="p">,</span>
+    <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences" title="moabb.analysis.meta_analysis.find_significant_differences" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">find_significant_differences</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">moabb.analysis.plotting</span> <span class="kn">import</span> <a href="../../generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot" title="moabb.analysis.plotting.paired_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">paired_plot</span></a><span class="p">,</span> <a href="../../generated/moabb.analysis.plotting.summary_plot.html#moabb.analysis.plotting.summary_plot" title="moabb.analysis.plotting.summary_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">summary_plot</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a>
+
+
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+<a href="../../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="loading-dataset">
+<h2>Loading Dataset<a class="headerlink" href="#loading-dataset" title="Permalink to this heading">#</a></h2>
+<p>Load 2 subjects of BNCI 2014-009 dataset, with 3 session each</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="get-data-optional">
+<h2>Get Data (optional)<a class="headerlink" href="#get-data-optional" title="Permalink to this heading">#</a></h2>
+<p>To get access to the EEG signals downloaded from the dataset, you could
+use <code class="docutils literal notranslate"><span class="pre">dataset.get_data([subject_id)</span></code> to obtain the EEG as MNE Epochs, stored
+in a dictionary of sessions and runs.
+The <code class="docutils literal notranslate"><span class="pre">paradigm.get_data(dataset=dataset,</span> <span class="pre">subjects=[subject_id])</span></code> allows to
+obtain the preprocessed EEG data, the labels and the meta information. By
+default, the EEG is return as a Numpy array. With <code class="docutils literal notranslate"><span class="pre">return_epochs=True</span></code>, MNE
+Epochs are returned.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_list</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sessions</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset.get_data" title="moabb.datasets.base.BaseDataset.get_data" class="sphx-glr-backref-module-moabb-datasets-base sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_list</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">,</span> <span class="n">meta</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">subjects</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_list</span></a><span class="p">)</span>
+<a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">,</span> <span class="n">meta</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">subjects</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_list</span></a><span class="p">,</span> <span class="n">return_epochs</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+</pre></div>
+</div>
+</section>
+<section id="a-simple-mne-pipeline">
+<h2>A Simple MNE Pipeline<a class="headerlink" href="#a-simple-mne-pipeline" title="Permalink to this heading">#</a></h2>
+<p>Using <code class="docutils literal notranslate"><span class="pre">return_epochs=True</span></code> in the evaluation, it is possible to design a
+pipeline based on MNE Epochs input. Let’s create a simple one, that
+reshape the input data from epochs, rescale the data and uses a logistic
+regression to classify the data. We will need to write a basic Transformer
+estimator, that complies with
+<a class="reference external" href="https://scikit-learn.org/stable/developers/develop.html">sklearn convention</a>.
+This transformer will extract the data from an input Epoch, and reshapes into
+2D array.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyVectorizer</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">BaseEstimator</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.TransformerMixin.html#sklearn.base.TransformerMixin" title="sklearn.base.TransformerMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TransformerMixin</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">arr</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">features_shape_</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">arr</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">()</span>
+        <span class="k">return</span> <span class="n">arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We will define a pipeline that is based on this new class, using a scaler
+and a logistic regression. This pipeline is evaluated across session using
+ROC-AUC metric.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_ppl</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_ppl</span></a><span class="p">[</span><span class="s2">&quot;MNE LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">MyVectorizer</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;l1&quot;</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_eval</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">return_epochs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mne_res</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">mne_eval</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_ppl</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-009-CrossSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession:  33%|###3      | 1/3 [00:03&lt;00:06,  3.19s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession:  67%|######6   | 2/3 [00:06&lt;00:03,  3.09s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:10&lt;00:00,  3.56s/it]
+BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:10&lt;00:00,  3.44s/it]
+</pre></div>
+</div>
+</section>
+<section id="advanced-mne-pipeline">
+<h2>Advanced MNE Pipeline<a class="headerlink" href="#advanced-mne-pipeline" title="Permalink to this heading">#</a></h2>
+<p>In some case, the MNE pipeline should have access to the original labels from
+the dataset. This is the case for the XDAWN code of MNE. One could pass
+<cite>mne_labels</cite> to evaluation in order to keep this label.
+As an example, we will define a pipeline that computes an XDAWN filter, rescale,
+then apply a logistic regression.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_adv</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_adv</span></a><span class="p">[</span><span class="s2">&quot;XDAWN LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://mne.tools/stable/generated/mne.preprocessing.Xdawn.html#mne.preprocessing.Xdawn" title="mne.preprocessing.Xdawn" class="sphx-glr-backref-module-mne-preprocessing sphx-glr-backref-type-py-class"><span class="n">Xdawn</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">reg</span><span class="o">=</span><span class="s2">&quot;ledoit_wolf&quot;</span><span class="p">,</span> <span class="n">correct_overlap</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <a href="https://mne.tools/stable/generated/mne.decoding.Vectorizer.html#mne.decoding.Vectorizer" title="mne.decoding.Vectorizer" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">Vectorizer</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;l1&quot;</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">adv_eval</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">return_epochs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">mne_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">adv_res</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">mne_eval</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne_adv</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-009-CrossSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession:  33%|###3      | 1/3 [00:03&lt;00:07,  3.53s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession:  67%|######6   | 2/3 [00:06&lt;00:03,  3.05s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:10&lt;00:00,  3.57s/it]
+BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:10&lt;00:00,  3.48s/it]
+</pre></div>
+</div>
+</section>
+<section id="numpy-based-pipeline">
+<h2>Numpy-based Pipeline<a class="headerlink" href="#numpy-based-pipeline" title="Permalink to this heading">#</a></h2>
+<p>For the comparison, we will define a Numpy-based pipeline that relies on
+pyriemann to estimate XDAWN-extended covariance matrices that are projected
+on the tangent space and classified with a logistic regression.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sk_ppl</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sk_ppl</span></a><span class="p">[</span><span class="s2">&quot;RG LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a><span class="p">(</span><span class="n">nfilter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">,</span> <span class="n">xdawn_estimator</span><span class="o">=</span><span class="s2">&quot;scm&quot;</span><span class="p">),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;l1&quot;</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sk_eval</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sk_res</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">sk_eval</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sk_ppl</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-009-CrossSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession:  33%|###3      | 1/3 [00:07&lt;00:14,  7.15s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession:  67%|######6   | 2/3 [00:14&lt;00:06,  6.99s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:20&lt;00:00,  6.93s/it]
+BNCI2014-009-CrossSession: 100%|##########| 3/3 [00:20&lt;00:00,  6.97s/it]
+</pre></div>
+</div>
+</section>
+<section id="combining-results">
+<h2>Combining Results<a class="headerlink" href="#combining-results" title="Permalink to this heading">#</a></h2>
+<p>Even if the results have been obtained by different evaluation processes, it
+is possible to combine the resulting DataFrames to analyze and plot the
+results.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">all_res</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">mne_res</span><span class="p">,</span> <span class="n">adv_res</span><span class="p">,</span> <span class="n">sk_res</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>We could compare the Euclidean and Riemannian performance using a <cite>paired_plot</cite></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot" title="moabb.analysis.plotting.paired_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">paired_plot</span></a><span class="p">(</span><span class="n">all_res</span><span class="p">,</span> <span class="s2">&quot;XDAWN LR&quot;</span><span class="p">,</span> <span class="s2">&quot;RG LR&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_mne_and_scikit_estimators_001.png" srcset="../../_images/sphx_glr_plot_mne_and_scikit_estimators_001.png" alt="plot mne and scikit estimators" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 1100x850 with 1 Axes&gt;
+</pre></div>
+</div>
+<p>All the results could be compared and statistical analysis could highlight the
+differences between pipelines.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">stats</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics" title="moabb.analysis.meta_analysis.compute_dataset_statistics" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">compute_dataset_statistics</span></a><span class="p">(</span><span class="n">all_res</span><span class="p">)</span>
+<span class="n">P</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences" title="moabb.analysis.meta_analysis.find_significant_differences" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">find_significant_differences</span></a><span class="p">(</span><span class="n">stats</span><span class="p">)</span>
+<a href="../../generated/moabb.analysis.plotting.summary_plot.html#moabb.analysis.plotting.summary_plot" title="moabb.analysis.plotting.summary_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">summary_plot</span></a><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_mne_and_scikit_estimators_002.png" srcset="../../_images/sphx_glr_plot_mne_and_scikit_estimators_002.png" alt="Algorithm comparison" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  45.979 seconds)</p>
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+<li class="toctree-l2"><a class="reference internal" href="../plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.benchmark.html">moabb.benchmark</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Advanced examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Select Electrodes and Resampling</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="select-electrodes-and-resampling">
+<span id="sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"></span><h1>Select Electrodes and Resampling<a class="headerlink" href="#select-electrodes-and-resampling" title="Permalink to this heading">#</a></h1>
+<p>Within paradigm, it is possible to restrict analysis only to a subset of
+electrodes and to resample to a specific sampling rate. There is also a
+utility function to select common electrodes shared between datasets.
+This tutorial demonstrates how to use this functionality.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Sylvain Chevallier &lt;sylvain.chevallier@uvsq.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LR</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb.analysis.plotting</span> <span class="k">as</span> <span class="nn">moabb_plt</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <a href="../../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets.utils</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.utils.find_intersecting_channels.html#moabb.datasets.utils.find_intersecting_channels" title="moabb.datasets.utils.find_intersecting_channels" class="sphx-glr-backref-module-moabb-datasets-utils sphx-glr-backref-type-py-function"><span class="n">find_intersecting_channels</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+</pre></div>
+</div>
+<section id="datasets">
+<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this heading">#</a></h2>
+<p>Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 sessions each</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a><span class="p">(),</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()]</span>
+<span class="k">for</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a>
+</pre></div>
+</div>
+</section>
+<section id="paradigm">
+<h2>Paradigm<a class="headerlink" href="#paradigm" title="Permalink to this heading">#</a></h2>
+<p>Restrict further analysis to specified channels, here C3, C4, and Cz.
+Also, use a specific resampling. In this example, all datasets are
+set to 200 Hz.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;C3&quot;</span><span class="p">,</span> <span class="s2">&quot;C4&quot;</span><span class="p">,</span> <span class="s2">&quot;Cz&quot;</span><span class="p">],</span> <span class="n">resample</span><span class="o">=</span><span class="mf">200.0</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>The evaluation is conducted on with CSP+LDA, only on the 3 electrodes, with
+a sampling rate of 200 Hz.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">)</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">csp_lda</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ts_lr</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;oas&quot;</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LR</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">({</span><span class="s2">&quot;csp+lda&quot;</span><span class="p">:</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">csp_lda</span></a><span class="p">,</span> <span class="s2">&quot;ts+lr&quot;</span><span class="p">:</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ts_lr</span></a><span class="p">})</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Zhou2016-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~1.7 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~1.7 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession:  50%|#####     | 1/2 [00:04&lt;00:04,  4.97s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~1.2 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession: 100%|##########| 2/2 [00:09&lt;00:00,  4.67s/it]
+Zhou2016-WithinSession: 100%|##########| 2/2 [00:09&lt;00:00,  4.71s/it]
+
+BNCI2014-001-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession:  50%|#####     | 1/2 [00:04&lt;00:04,  4.25s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession: 100%|##########| 2/2 [00:08&lt;00:00,  4.08s/it]
+BNCI2014-001-WithinSession: 100%|##########| 2/2 [00:08&lt;00:00,  4.10s/it]
+      score      time  samples subject  ... channels  n_sessions   dataset pipeline
+0  0.844823  0.058944    119.0       1  ...        3           3  Zhou2016    ts+lr
+1  0.900000  0.049391    100.0       1  ...        3           3  Zhou2016    ts+lr
+2  0.950000  0.049339    100.0       1  ...        3           3  Zhou2016    ts+lr
+3  0.918000  0.049579    100.0       2  ...        3           3  Zhou2016    ts+lr
+4  0.817284  0.045368     90.0       2  ...        3           3  Zhou2016    ts+lr
+
+[5 rows x 9 columns]
+</pre></div>
+</div>
+</section>
+<section id="electrode-selection">
+<h2>Electrode Selection<a class="headerlink" href="#electrode-selection" title="Permalink to this heading">#</a></h2>
+<p>It is possible to select the electrodes that are shared by all datasets
+using the <cite>find_intersecting_channels</cite> function. Datasets that have 0
+overlap with others are discarded. It returns the set of common channels,
+as well as the list of datasets with valid channels.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">electrodes</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.utils.find_intersecting_channels.html#moabb.datasets.utils.find_intersecting_channels" title="moabb.datasets.utils.find_intersecting_channels" class="sphx-glr-backref-module-moabb-datasets-utils sphx-glr-backref-type-py-function"><span class="n">find_intersecting_channels</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">)</span>
+<a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;resample&quot;</span>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">({</span><span class="s2">&quot;csp+lda&quot;</span><span class="p">:</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">csp_lda</span></a><span class="p">,</span> <span class="s2">&quot;ts+lr&quot;</span><span class="p">:</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ts_lr</span></a><span class="p">})</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Searching dataset: Zhou2016
+Searching dataset: BNCI2014_001
+
+Zhou2016-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~1.7 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~1.7 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession:  50%|#####     | 1/2 [00:04&lt;00:04,  4.88s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~1.2 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~1.4 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession: 100%|##########| 2/2 [00:09&lt;00:00,  4.49s/it]
+Zhou2016-WithinSession: 100%|##########| 2/2 [00:09&lt;00:00,  4.55s/it]
+
+BNCI2014-001-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession:  50%|#####     | 1/2 [00:04&lt;00:04,  4.13s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~582 kB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession: 100%|##########| 2/2 [00:08&lt;00:00,  4.14s/it]
+BNCI2014-001-WithinSession: 100%|##########| 2/2 [00:08&lt;00:00,  4.14s/it]
+      score      time  samples subject  ... channels  n_sessions   dataset pipeline
+0  0.865657  0.058112    119.0       1  ...        3           3  Zhou2016    ts+lr
+1  0.916000  0.049458    100.0       1  ...        3           3  Zhou2016    ts+lr
+2  0.932000  0.050020    100.0       1  ...        3           3  Zhou2016    ts+lr
+3  0.924000  0.050490    100.0       2  ...        3           3  Zhou2016    ts+lr
+4  0.829630  0.045536     90.0       2  ...        3           3  Zhou2016    ts+lr
+
+[5 rows x 9 columns]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Compare the obtained results with the two pipelines, CSP+LDA and logistic
+regression computed in the tangent space of the covariance matrices.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a> <span class="o">=</span> <a href="../../generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot" title="moabb.analysis.plotting.paired_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">moabb_plt</span><span class="o">.</span><span class="n">paired_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="s2">&quot;csp+lda&quot;</span><span class="p">,</span> <span class="s2">&quot;ts+lr&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_select_electrodes_resample_001.png" srcset="../../_images/sphx_glr_plot_select_electrodes_resample_001.png" alt="plot select electrodes resample" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  41.373 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  429 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-examples-plot-select-electrodes-resample-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/83c876b60654b8a0b9ebaf6714ef3607/plot_select_electrodes_resample.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_select_electrodes_resample.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/657e87726c4d308a5b176dbb4277773e/plot_select_electrodes_resample.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_select_electrodes_resample.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
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+        <p class="prev-next-title">Statistical Analysis</p>
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+    <i class="fa-solid fa-list"></i> On this page
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#datasets">Datasets</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#paradigm">Paradigm</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#electrode-selection">Electrode Selection</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot Results</a></li>
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diff --git a/docs/auto_examples/advanced_examples/plot_statistical_analysis.html b/docs/auto_examples/advanced_examples/plot_statistical_analysis.html
new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
+
+
+
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+<nav aria-label="Breadcrumbs">
+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../../overview.html" class="nav-link">Documentation overview</a></li>
+    
+    
+    <li class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Advanced examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Statistical Analysis</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="statistical-analysis">
+<span id="sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"></span><h1>Statistical Analysis<a class="headerlink" href="#statistical-analysis" title="Permalink to this heading">#</a></h1>
+<p>The MOABB codebase comes with convenience plotting utilities and some
+statistical testing. This tutorial focuses on what those exactly are and how
+they can be used.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Vinay Jayaram &lt;vinayjayaram13@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+<span class="c1"># sphinx_gallery_thumbnail_number = -2</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">import</span> <span class="nn">moabb.analysis.plotting</span> <span class="k">as</span> <span class="nn">moabb_plt</span>
+<span class="kn">from</span> <span class="nn">moabb.analysis.meta_analysis</span> <span class="kn">import</span> <span class="p">(</span>  <span class="c1"># noqa: E501</span>
+    <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics" title="moabb.analysis.meta_analysis.compute_dataset_statistics" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">compute_dataset_statistics</span></a><span class="p">,</span>
+    <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences" title="moabb.analysis.meta_analysis.find_significant_differences" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">find_significant_differences</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="results-generation">
+<h2>Results Generation<a class="headerlink" href="#results-generation" title="Permalink to this heading">#</a></h2>
+<p>First we need to set up a paradigm, dataset list, and some pipelines to
+test. This is explored more in the examples – we choose left vs right
+imagery paradigm with a single bandpass. There is only one dataset here but
+any number can be added without changing this workflow.</p>
+</section>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.</p>
+<p>The CSP implementation from MNE is used. We selected 8 CSP components, as
+usually done in the literature.</p>
+<p>The Riemannian geometry pipeline consists in covariance estimation, tangent
+space mapping and finally a logistic regression for the classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">())</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;CSP+LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">())</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
+The evaluation will return a DataFrame containing a single AUC score for
+each subject / session of the dataset, and for each pipeline.</p>
+<p>Results are saved into the database, so that if you add a new pipeline, it
+will not run again the evaluation unless a parameter has changed. Results can
+be overwritten if necessary.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">4</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to False if we want to use cached results</span>
+<a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;stats&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-CrossSession:   0%|          | 0/4 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession:  25%|##5       | 1/4 [00:06&lt;00:18,  6.16s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession:  50%|#####     | 2/4 [00:12&lt;00:12,  6.13s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession:  75%|#######5  | 3/4 [00:18&lt;00:06,  6.06s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession: 100%|##########| 4/4 [00:24&lt;00:00,  6.06s/it]
+BNCI2014-001-CrossSession: 100%|##########| 4/4 [00:24&lt;00:00,  6.08s/it]
+</pre></div>
+</div>
+</section>
+<section id="moabb-plotting">
+<h2>MOABB Plotting<a class="headerlink" href="#moabb-plotting" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results using some of the convenience methods within the
+toolkit.  The score_plot visualizes all the data with one score per subject
+for every dataset and pipeline.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a> <span class="o">=</span> <a href="../../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">moabb_plt</span><span class="o">.</span><span class="n">score_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_statistical_analysis_001.png" srcset="../../_images/sphx_glr_plot_statistical_analysis_001.png" alt="Scores per dataset and algorithm" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/analysis/plotting.py:70: UserWarning: The palette list has more values (6) than needed (4), which may not be intended.
+  sea.stripplot(
+</pre></div>
+</div>
+<p>For a comparison of two algorithms, there is the paired_plot, which plots
+performance in one versus the performance in the other over all chosen
+datasets. Note that there is only one score per subject, regardless of the
+number of sessions.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a> <span class="o">=</span> <a href="../../generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot" title="moabb.analysis.plotting.paired_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">moabb_plt</span><span class="o">.</span><span class="n">paired_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="s2">&quot;CSP+LDA&quot;</span><span class="p">,</span> <span class="s2">&quot;RG+LDA&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_statistical_analysis_002.png" srcset="../../_images/sphx_glr_plot_statistical_analysis_002.png" alt="plot statistical analysis" class = "sphx-glr-single-img"/></section>
+<section id="statistical-testing-and-further-plots">
+<h2>Statistical Testing and Further Plots<a class="headerlink" href="#statistical-testing-and-further-plots" title="Permalink to this heading">#</a></h2>
+<p>If the statistical significance of results is of interest, the method
+compute_dataset_statistics allows one to show a meta-analysis style plot as
+well. For an overview of how all algorithms perform in comparison with each
+other, the method find_significant_differences and the summary_plot are
+possible.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">stats</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics" title="moabb.analysis.meta_analysis.compute_dataset_statistics" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">compute_dataset_statistics</span></a><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<span class="n">P</span><span class="p">,</span> <span class="n">T</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences" title="moabb.analysis.meta_analysis.find_significant_differences" class="sphx-glr-backref-module-moabb-analysis-meta_analysis sphx-glr-backref-type-py-function"><span class="n">find_significant_differences</span></a><span class="p">(</span><span class="n">stats</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The meta-analysis style plot shows the standardized mean difference within
+each tested dataset for the two algorithms in question, in addition to a
+meta-effect and significance both per-dataset and overall.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a> <span class="o">=</span> <a href="../../generated/moabb.analysis.plotting.meta_analysis_plot.html#moabb.analysis.plotting.meta_analysis_plot" title="moabb.analysis.plotting.meta_analysis_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">moabb_plt</span><span class="o">.</span><span class="n">meta_analysis_plot</span></a><span class="p">(</span><span class="n">stats</span><span class="p">,</span> <span class="s2">&quot;CSP+LDA&quot;</span><span class="p">,</span> <span class="s2">&quot;RG+LDA&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_statistical_analysis_003.png" srcset="../../_images/sphx_glr_plot_statistical_analysis_003.png" alt="< RG+LDA better                                                                              CSP+LDA better >, p-value" class = "sphx-glr-single-img"/><p>The summary plot shows the effect and significance related to the hypothesis
+that the algorithm on the y-axis significantly outperformed the algorithm on
+the x-axis over all datasets</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.analysis.plotting.summary_plot.html#moabb.analysis.plotting.summary_plot" title="moabb.analysis.plotting.summary_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">moabb_plt</span><span class="o">.</span><span class="n">summary_plot</span></a><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_statistical_analysis_004.png" srcset="../../_images/sphx_glr_plot_statistical_analysis_004.png" alt="Algorithm comparison" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  28.585 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  308 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-examples-plot-statistical-analysis-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/fc103af82bd37a6a36f57f863e9fd4aa/plot_statistical_analysis.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_statistical_analysis.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/33bee7d7204a7cb56293bd1d838b63f8/plot_statistical_analysis.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_statistical_analysis.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
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+    <div class="footer-article-item"><!-- Previous / next buttons -->
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#results-generation">Results Generation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipelines">Create Pipelines</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb-plotting">MOABB Plotting</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#statistical-testing-and-further-plots">Statistical Testing and Further Plots</a></li>
+</ul>
+  </nav></div>
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+          <footer class="bd-footer-content">
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+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
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diff --git a/docs/auto_examples/advanced_examples/sg_execution_times.html b/docs/auto_examples/advanced_examples/sg_execution_times.html
new file mode 100644
index 00000000..dff76b53
--- /dev/null
+++ b/docs/auto_examples/advanced_examples/sg_execution_times.html
@@ -0,0 +1,546 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../../_static/moabb_logo.png" type="image/png">
+    <title>Computation times &#8212; moabb 1.0.0-dev documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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+    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
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+      </div>
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+        
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+<div class="header-article-items header-article__inner">
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+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    <li class="breadcrumb-item active" aria-current="page">Computation times</li>
+  </ul>
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+                  
+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-advanced-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
+<p><strong>02:11.578</strong> total execution time for <strong>auto_examples_advanced_examples</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mne_and_scikit_estimators.py</span></code>)</p></td>
+<td><p>00:45.979</p></td>
+<td><p>339.3 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_select_electrodes_resample.py</span></code>)</p></td>
+<td><p>00:41.373</p></td>
+<td><p>429.1 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_statistical_analysis.py</span></code>)</p></td>
+<td><p>00:28.585</p></td>
+<td><p>307.7 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid_search_withinsession.py</span></code>)</p></td>
+<td><p>00:13.381</p></td>
+<td><p>257.3 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_filterbank_csp_vs_csp.py</span></code>)</p></td>
+<td><p>00:02.261</p></td>
+<td><p>8.8 MB</p></td>
+</tr>
+</tbody>
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diff --git a/docs/auto_examples/changing_download_directory.html b/docs/auto_examples/changing_download_directory.html
new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
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+<nav aria-label="Breadcrumbs">
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+      <a href="../index.html" class="nav-link" aria-label="Home">
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+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Change Download Directory</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-changing-download-directory-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="change-download-directory">
+<span id="sphx-glr-auto-examples-changing-download-directory-py"></span><h1>Change Download Directory<a class="headerlink" href="#change-download-directory" title="Permalink to this heading">#</a></h1>
+<p>This is a minimal example to demonstrate how to change the default data
+download directory to a custom path/location.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Divyesh Narayanan &lt;divyesh.narayanan@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">os.path</span> <span class="k">as</span> <span class="nn">osp</span>
+
+<span class="kn">from</span> <span class="nn">mne</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.get_config.html#mne.get_config" title="mne.get_config" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">get_config</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb.utils</span> <span class="kn">import</span> <a href="../generated/moabb.set_download_dir.html#moabb.set_download_dir" title="moabb.set_download_dir" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_download_dir</span></a>
+</pre></div>
+</div>
+<p>You can choose to change the download directory to any path of your choice.
+If the path/folder doesn’t exist, it will be created for you.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">original_path</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.get_config.html#mne.get_config" title="mne.get_config" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">get_config</span></a><span class="p">(</span><span class="s2">&quot;MNE_DATA&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The download directory is currently </span><span class="si">{</span><span class="n">original_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">new_path</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/os.path.html#os.path.join" title="os.path.join" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">osp</span><span class="o">.</span><span class="n">join</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/os.path.html#os.path.expanduser" title="os.path.expanduser" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">osp</span><span class="o">.</span><span class="n">expanduser</span></a><span class="p">(</span><span class="s2">&quot;~&quot;</span><span class="p">),</span> <span class="s2">&quot;mne_data_test&quot;</span><span class="p">)</span>
+<a href="../generated/moabb.set_download_dir.html#moabb.set_download_dir" title="moabb.set_download_dir" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_download_dir</span></a><span class="p">(</span><span class="n">new_path</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>You could verify that the MNE config has been changed correctly</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">check_path</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.get_config.html#mne.get_config" title="mne.get_config" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">get_config</span></a><span class="p">(</span><span class="s2">&quot;MNE_DATA&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Now the download directory has been changed to </span><span class="si">{</span><span class="n">check_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Set the directory back to default location</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.set_download_dir.html#moabb.set_download_dir" title="moabb.set_download_dir" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_download_dir</span></a><span class="p">(</span><span class="n">original_path</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-changing-download-directory-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/93f59072dcc594921985afeb50d4cf2b/changing_download_directory.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">changing_download_directory.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/c231a2e535b6091b8fee66bcebfa041c/changing_download_directory.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">changing_download_directory.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+                </article>
+              
+              
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+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
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+        <p class="prev-next-title">Examples</p>
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+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">Benchmarking with MOABB showing the CO2 footprint</p>
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+    
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\ No newline at end of file
diff --git a/docs/auto_examples/example_codecarbon.html b/docs/auto_examples/example_codecarbon.html
new file mode 100644
index 00000000..8132e206
--- /dev/null
+++ b/docs/auto_examples/example_codecarbon.html
@@ -0,0 +1,863 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../_static/moabb_logo.png" type="image/png">
+    <title>Benchmarking with MOABB showing the CO2 footprint &#8212; moabb 1.0.0-dev documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM3.html">moabb.datasets.MAMEM3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_SSVEP.html">moabb.datasets.Lee2019_SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2015.html">moabb.datasets.Thielen2015</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2021.html">moabb.datasets.Thielen2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP100.html">moabb.datasets.CastillosBurstVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.BaseDataset.html">moabb.datasets.base.BaseDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.CacheConfig.html">moabb.datasets.base.CacheConfig</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeDataset.html">moabb.datasets.fake.FakeDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeVirtualRealityDataset.html">moabb.datasets.fake.FakeVirtualRealityDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_path.html">moabb.datasets.download.data_path</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_dl.html">moabb.datasets.download.data_dl</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_issue_request.html">moabb.datasets.download.fs_issue_request</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_list.html">moabb.datasets.download.fs_get_file_list</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_hash.html">moabb.datasets.download.fs_get_file_hash</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_id.html">moabb.datasets.download.fs_get_file_id</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_name.html">moabb.datasets.download.fs_get_file_name</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.dataset_search.html">moabb.datasets.utils.dataset_search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.find_intersecting_channels.html">moabb.datasets.utils.find_intersecting_channels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014a_Il.html">moabb.datasets.compound_dataset.BI2014a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Benchmarking with MOABB showing the CO2 footprint</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
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+</div>
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+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-example-codecarbon-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-with-moabb-showing-the-co2-footprint">
+<span id="sphx-glr-auto-examples-example-codecarbon-py"></span><h1>Benchmarking with MOABB showing the CO2 footprint<a class="headerlink" href="#benchmarking-with-moabb-showing-the-co2-footprint" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use MOABB to track the CO2 footprint
+using <a class="reference external" href="https://codecarbon.io/">CodeCarbon library</a>.
+For this example, we will use only one
+dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets. Due to limitation of online documentation
+generation, the results is computed on a local cluster but could be easily
+replicated on your infrastructure.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Igor Carrara &lt;igor.carrara@inria.fr&gt;</span>
+<span class="c1">#          Bruno Aristimunha &lt;b.aristimunha@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">,</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.analysis.plotting</span> <span class="kn">import</span> <span class="n">codecarbon_plot</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="loading-the-pipelines">
+<h2>Loading the pipelines<a class="headerlink" href="#loading-the-pipelines" title="Permalink to this heading">#</a></h2>
+<p>To run this example we use several pipelines, ML and DL (Keras) and also
+pipelines that need an optimization of the hyperparameter.
+All this different pipelines are stored in <code class="docutils literal notranslate"><span class="pre">pipelines_codecarbon</span></code></p>
+</section>
+<section id="selecting-the-datasets-optional">
+<h2>Selecting the datasets (optional)<a class="headerlink" href="#selecting-the-datasets-optional" title="Permalink to this heading">#</a></h2>
+<p>If you want to limit your benchmark on a subset of datasets, you can use the
+<code class="docutils literal notranslate"><span class="pre">include_datasets</span></code> and <code class="docutils literal notranslate"><span class="pre">exclude_datasets</span></code> arguments. You will need either
+to provide the dataset’s object, or a dataset’s code. To get the list of
+available dataset’s code for a given paradigm, you can use the following
+command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">paradigm</span> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">paradigm</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">code</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>In this example, we will use only the last dataset, ‘Zhou 2016’, considering
+only the first subject.</p>
+</section>
+<section id="running-the-benchmark">
+<h2>Running the benchmark<a class="headerlink" href="#running-the-benchmark" title="Permalink to this heading">#</a></h2>
+<p>The benchmark is run using the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> function. You need to specify the
+folder containing the pipelines to use, the kind of evaluation and the paradigm
+to use. By default, the benchmark will use all available datasets for all
+paradigms listed in the pipelines. You could restrict to specific evaluation
+and paradigm using the <code class="docutils literal notranslate"><span class="pre">evaluations</span></code> and <code class="docutils literal notranslate"><span class="pre">paradigms</span></code> arguments.</p>
+<p>To save computation time, the results are cached. If you want to re-run the
+benchmark, you can set the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<p>It is possible to indicate the folder to cache the results and the one to
+save the analysis &amp; figures. By default, the results are saved in the
+<code class="docutils literal notranslate"><span class="pre">results</span></code> folder, and the analysis &amp; figures are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code>
+folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Zhou2016</span></a><span class="p">()</span>
+<span class="n">dataset2</span> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">dataset2</span><span class="o">.</span><span class="n">subject_list</span> <span class="o">=</span> <span class="n">dataset2</span><span class="o">.</span><span class="n">subject_list</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">,</span> <span class="n">dataset2</span><span class="p">]</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">(</span>
+    <span class="n">pipelines</span><span class="o">=</span><span class="s2">&quot;./pipelines_codecarbon/&quot;</span><span class="p">,</span>
+    <span class="n">evaluations</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;WithinSession&quot;</span><span class="p">],</span>
+    <span class="n">paradigms</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LeftRightImagery&quot;</span><span class="p">],</span>
+    <span class="n">include_datasets</span><span class="o">=</span><span class="n">datasets</span><span class="p">,</span>
+    <span class="n">results</span><span class="o">=</span><span class="s2">&quot;./results/&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">output</span><span class="o">=</span><span class="s2">&quot;./benchmark/&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>Benchmark prints a summary of the results. Detailed results are saved in a
+pandas dataframe, and can be used to generate figures. The analysis &amp; figures
+are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+
+<span class="n">order_list</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;CSP + SVM&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Tangent Space LR&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;EN Grid&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CSP + LDA Grid&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Keras_EEGNet_8_2&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="plotting-the-results">
+<h2>Plotting the results<a class="headerlink" href="#plotting-the-results" title="Permalink to this heading">#</a></h2>
+<p>We can plot the results using the <code class="docutils literal notranslate"><span class="pre">codecarbon_plot</span></code> function, generated
+below. This function takes the dataframe returned by the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code>
+function as input, and returns a pyplot figure.
+The <code class="docutils literal notranslate"><span class="pre">order_list</span></code> argument is used to specify the order of the pipelines in
+the plot.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">codecarbon_plot</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">order_list</span><span class="p">,</span> <span class="n">country</span><span class="o">=</span><span class="s2">&quot;(France)&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl>
+<dt>The result expected will be the following image, but varying depending on the</dt><dd><p>machine and the country used to run the example.</p>
+<img alt="carbon_example" class="align-center" src="../_images/example_codecarbon.png" />
+</dd>
+</dl>
+<hr class="docutils" />
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-example-codecarbon-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/1fae02a0d2f8a8b38412328b11979e03/example_codecarbon.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">example_codecarbon.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/f4558bdb453cb198095be347c0f12fe0/example_codecarbon.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">example_codecarbon.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-pipelines">Loading the pipelines</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#selecting-the-datasets-optional">Selecting the datasets (optional)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#running-the-benchmark">Running the benchmark</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-the-results">Plotting the results</a></li>
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../../paper_results.html">
+                        The largest EEG benchmark
+                      </a>
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+                
+
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+                        Datasets
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+    
+    
+      <div class="sidebar-header-items__center">
+        
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../../paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../../dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../../install/install.html">
+                        Install
+                      </a>
+                    </li>
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+<p><a class="reference internal" href="plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
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diff --git a/docs/auto_examples/external/plot_learning_curve_p300_external.html b/docs/auto_examples/external/plot_learning_curve_p300_external.html
new file mode 100644
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+    
+    <li class="breadcrumb-item active" aria-current="page">Within Session P300 with Learning Curve</li>
+  </ul>
+</nav>
+</div>
+      
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+              
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+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="within-session-p300-with-learning-curve">
+<span id="sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"></span><h1>Within Session P300 with Learning Curve<a class="headerlink" href="#within-session-p300-with-learning-curve" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to perform a within session analysis while also
+creating learning curves for a P300 dataset.
+Additionally, we will evaluate external code. Make sure to have tdlda installed
+, which can be found in requirements_external.txt</p>
+<p>We will compare three pipelines :</p>
+<ul class="simple">
+<li><p>Riemannian geometry</p></li>
+<li><p>Jumping Means-based Linear Discriminant Analysis</p></li>
+<li><p>Time-Decoupled Linear Discriminant Analysis</p></li>
+</ul>
+<p>We will use the P300 paradigm, which uses the AUC as metric.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Jan Sosulski</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">tdlda</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TimeDecoupledLda</span></a>
+<span class="kn">from</span> <span class="nn">tdlda</span> <span class="kn">import</span> <span class="n">Vectorizer</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">JumpingMeansVectorizer</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a>
+
+
+<span class="c1"># getting rid of the warnings about the future (on s&#39;en fout !)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+
+<a href="../../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-pipelines">
+<h2>Create pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing_sampling_rate</span></a> <span class="o">=</span> <span class="mi">128</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+
+<span class="c1"># We have to do this because the classes are called &#39;Target&#39; and &#39;NonTarget&#39;</span>
+<span class="c1"># but the evaluation function uses a LabelEncoder, transforming them</span>
+<span class="c1"># to 0 and 1</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels_dict</span></a> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Target&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;NonTarget&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
+
+<span class="c1"># Riemannian geometry based classification</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a><span class="p">(</span><span class="n">nfilter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">,</span> <span class="n">xdawn_estimator</span><span class="o">=</span><span class="s2">&quot;scm&quot;</span><span class="p">),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># Simple LDA pipeline using averaged feature values in certain time intervals</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">jumping_mean_ivals</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">[</span><span class="mf">0.10</span><span class="p">,</span> <span class="mf">0.139</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.14</span><span class="p">,</span> <span class="mf">0.169</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.17</span><span class="p">,</span> <span class="mf">0.199</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.20</span><span class="p">,</span> <span class="mf">0.229</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.23</span><span class="p">,</span> <span class="mf">0.269</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.27</span><span class="p">,</span> <span class="mf">0.299</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.30</span><span class="p">,</span> <span class="mf">0.349</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.409</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.41</span><span class="p">,</span> <span class="mf">0.449</span><span class="p">],</span>
+    <span class="p">[</span><span class="mf">0.45</span><span class="p">,</span> <span class="mf">0.499</span><span class="p">],</span>
+<span class="p">]</span>
+<span class="n">jmv</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">JumpingMeansVectorizer</span></a><span class="p">(</span>
+    <span class="n">fs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing_sampling_rate</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">jumping_mean_ivals</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">jumping_mean_ivals</span></a>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;JM+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">jmv</span><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">))</span>
+
+<span class="c1"># Time-decoupled Covariance classifier, needs information about number of</span>
+<span class="c1"># channels and time intervals</span>
+<span class="n">c</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TimeDecoupledLda</span></a><span class="p">(</span><span class="n">N_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">N_times</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="c1"># TD-LDA needs to know about the used jumping means intervals</span>
+<span class="n">c</span><span class="o">.</span><span class="n">preproc</span> <span class="o">=</span> <span class="n">jmv</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;JM+TD-LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">jmv</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigm (P300) and use the BNCI 2014-009 dataset for it.
+The evaluation will return a dataframe containing AUCs for each permutation
+and dataset size.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a><span class="p">(</span><span class="n">resample</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing_sampling_rate</span></a><span class="p">)</span>
+<a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a><span class="p">()</span>
+<span class="c1"># Remove the slicing of the subject list to evaluate multiple subjects</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">policy</span><span class="o">=</span><span class="s2">&quot;ratio&quot;</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.geomspace.html#numpy.geomspace" title="numpy.geomspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span></a><span class="p">(</span><span class="mf">0.02</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="c1"># When the training data is sparse, perform more permutations than when we have</span>
+<span class="c1"># a lot of data</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">floor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.geomspace.html#numpy.geomspace" title="numpy.geomspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span></a><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">])))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="p">)</span>
+<span class="c1"># Guarantee reproducibility</span>
+<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">7536298</span><span class="p">)</span>
+<a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="p">,</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples_lr&quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[20 12  7  5  3  2]
+
+BNCI2014-009-WithinSession:   0%|          | 0/1 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+
+BNCI2014-009-WithinSession: 100%|##########| 1/1 [01:56&lt;00:00, 116.33s/it]
+BNCI2014-009-WithinSession: 100%|##########| 1/1 [01:56&lt;00:00, 116.33s/it]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">)</span>
+
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;data_size&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">results</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;data_size&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">errbar_meaning</span></a> <span class="o">=</span> <span class="s2">&quot;subjects&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">&quot;permutations&quot;</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">title_str</span></a> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Errorbar shows Mean-CI across </span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">errbar_meaning</span></a><span class="si">}</span><span class="s2">&quot;</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Amount of training samples&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">title_str</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_learning_curve_p300_external_001.png" srcset="../../_images/sphx_glr_plot_learning_curve_p300_external_001.png" alt="Errorbar shows Mean-CI across permutations" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  58.646 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  13 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-external-plot-learning-curve-p300-external-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1eb996e21dc5546952cdf5d77959e6ad/plot_learning_curve_p300_external.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_learning_curve_p300_external.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e34acbbaad3fb8832beb7fa8e4557c94/plot_learning_curve_p300_external.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_learning_curve_p300_external.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
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+                <footer class="bd-footer-article">
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+<div class="footer-article-items footer-article__inner">
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+      <i class="fa-solid fa-angle-left"></i>
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+        <p class="prev-next-title">External examples</p>
+      </div>
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipelines">Create pipelines</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot Results</a></li>
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diff --git a/docs/auto_examples/external/sg_execution_times.html b/docs/auto_examples/external/sg_execution_times.html
new file mode 100644
index 00000000..263861e1
--- /dev/null
+++ b/docs/auto_examples/external/sg_execution_times.html
@@ -0,0 +1,530 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
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+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../../_static/moabb_logo.png" type="image/png">
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+                  
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+<span id="sphx-glr-auto-examples-external-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
+<p><strong>01:58.646</strong> total execution time for <strong>auto_examples_external</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_learning_curve_p300_external.py</span></code>)</p></td>
+<td><p>01:58.646</p></td>
+<td><p>12.9 MB</p></td>
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+<li class="toctree-l2"><a class="reference internal" href="changing_download_directory.html">Change Download Directory</a></li>
+<li class="toctree-l2"><a class="reference internal" href="example_codecarbon.html">Benchmarking with MOABB showing the CO2 footprint</a></li>
+<li class="toctree-l2"><a class="reference internal" href="load_model.html">Load Model (Scikit, Pytorch, Keras) with MOABB</a></li>
+<li class="toctree-l2"><a class="reference internal" href="noplot_bids_conversion.html">Convert a MOABB dataset to BIDS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="noplot_phmd_ml_spectrum.html">Spectral analysis of the trials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="noplot_vr_pc_p300_different_epoch_size.html">Changing epoch size in P300 VR dataset</a></li>
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+
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_DL.html">Benchmarking on MOABB with Tensorflow deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_braindecode.html">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_grid_search.html">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_subject_ssvep.html">Cross-Subject SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_disk_cache.html">Cache on disk intermediate data processing states</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_explore_paradigm.html">Explore Paradigm Object</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_fixed_interval_windows.html">Fixed interval windows processing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_p300.html">Within Session P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_ssvep.html">Within Session SSVEP</a></li>
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+  <section id="examples">
+<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this heading">#</a></h1>
+<p>These examples demonstrate how to use MOABB, and its main concepts the
+<code class="docutils literal notranslate"><span class="pre">dataset</span></code>, <code class="docutils literal notranslate"><span class="pre">paradigm</span></code> and <code class="docutils literal notranslate"><span class="pre">evaluation</span></code>. Those examples are using
+only a small number of subjects, and a small number of sessions, to
+keep the execution time short. In practice, you should use all the
+subjects and sessions available in the dataset.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This is a minimal example to demonstrate how to change the default data download directory to a..."><img alt="Change Download Directory" src="../_images/sphx_glr_changing_download_directory_thumb.png" />
+<p><a class="reference internal" href="changing_download_directory.html#sphx-glr-auto-examples-changing-download-directory-py"><span class="std std-ref">Change Download Directory</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Change Download Directory</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to track the CO2 footprint using `CodeCarbon library &lt;https..."><img alt="Benchmarking with MOABB showing the CO2 footprint" src="../_images/sphx_glr_example_codecarbon_thumb.png" />
+<p><a class="reference internal" href="example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB showing the CO2 footprint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use load the pretrained pipeline in MOABB."><img alt="Load Model (Scikit, Pytorch, Keras) with MOABB" src="../_images/sphx_glr_load_model_thumb.png" />
+<p><a class="reference internal" href="load_model.html#sphx-glr-auto-examples-load-model-py"><span class="std std-ref">Load Model (Scikit, Pytorch, Keras) with MOABB</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load Model (Scikit, Pytorch, Keras) with MOABB</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The Brain Imaging Data Structure (BIDS) format is standard for storing neuroimaging data. It fo..."><img alt="Convert a MOABB dataset to BIDS" src="../_images/sphx_glr_noplot_bids_conversion_thumb.png" />
+<p><a class="reference internal" href="noplot_bids_conversion.html#sphx-glr-auto-examples-noplot-bids-conversion-py"><span class="std std-ref">Convert a MOABB dataset to BIDS</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convert a MOABB dataset to BIDS</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to perform spectral analysis on epochs extracted from a specific ..."><img alt="Spectral analysis of the trials" src="../_images/sphx_glr_noplot_phmd_ml_spectrum_thumb.png" />
+<p><a class="reference internal" href="noplot_phmd_ml_spectrum.html#sphx-glr-auto-examples-noplot-phmd-ml-spectrum-py"><span class="std std-ref">Spectral analysis of the trials</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Spectral analysis of the trials</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="======================================= Changing epoch size in P300 VR dataset ================..."><img alt="Example of P300 classification with different epoch size." src="../_images/sphx_glr_noplot_vr_pc_p300_different_epoch_size_thumb.png" />
+<p><a class="reference external" href="https://neurotechx.github.io/moabb/auto_examples/noplot_vr_pc_p300_different_epoch_size.html#sphx-glr-auto-examples-noplot-vr-pc-p300-different-epoch-size-py" title="(in moabb v1.0.0)"><span class="xref std std-ref">sphx_glr_auto_examples_noplot_vr_pc_p300_different_epoch_size.py</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Example of P300 classification with different epoch size.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Benchmarking with MOABB with Grid Search" src="../_images/sphx_glr_plot_benchmark_grid_search_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark_grid_search.html#sphx-glr-auto-examples-plot-benchmark-grid-search-py"><span class="std std-ref">Benchmarking with MOABB with Grid Search</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB with Grid Search</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="advanced-examples">
+<h1>Advanced examples<a class="headerlink" href="#advanced-examples" title="Permalink to this heading">#</a></h1>
+<p>These examples show various advanced topics:</p>
+<ul class="simple">
+<li><p>using scikit-learn pipeline with MNE inputs</p></li>
+<li><p>selecting electrodes or resampling signal</p></li>
+<li><p>using filterbank approach in motor imagery</p></li>
+<li><p>apply statistics for meta-analysis</p></li>
+<li><p>using a gridsearch in within-subject decoding</p></li>
+</ul>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div></div></section>
+<section id="external-examples">
+<h1>External examples<a class="headerlink" href="#external-examples" title="Permalink to this heading">#</a></h1>
+<p>You need to install external dependencies to run these examples. These consist mostly of
+various classifier implementations. When using poetry, you can use</p>
+<p><code class="docutils literal notranslate"><span class="pre">poetry</span> <span class="pre">install</span> <span class="pre">--extras</span> <span class="pre">external</span></code></p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div></div></section>
+<section id="evaluation-with-learning-curve">
+<h1>Evaluation with learning curve<a class="headerlink" href="#evaluation-with-learning-curve" title="Permalink to this heading">#</a></h1>
+<p>These examples demonstrate how to make evaluations using only a subset of
+available example. For example, if you consider a dataset with 100 trials for
+each class, you could evaluate several pipelines by using only a fraction of
+these trials. To ensure the robustness of the results, you need to specify the
+number of permutations. If you use 10 trials per class and 20 permutations,
+each pipeline will be evaluated on a subset of 10 trials chosen randomly, that
+will be repeated 20 times with different trial subsets.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+<div class="sphx-glr-footer sphx-glr-footer-gallery docutils container">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">auto_examples_python.zip</span></code></a></p>
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+<p>These examples demonstrate how to make evaluations using only a subset of
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+<p><a class="reference internal" href="plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
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+<div class="header-article-items header-article__inner">
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+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
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+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Evaluation with learning curve</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Within Session Motor Imagery with Learning Curve</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="within-session-motor-imagery-with-learning-curve">
+<span id="sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"></span><h1>Within Session Motor Imagery with Learning Curve<a class="headerlink" href="#within-session-motor-imagery-with-learning-curve" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to perform a within session motor imagery analysis on the
+very popular dataset 2a from the BCI competition IV.</p>
+<p>We will compare two pipelines :</p>
+<ul class="simple">
+<li><p>CSP + LDA</p></li>
+<li><p>Riemannian Geometry + Logistic Regression</p></li>
+</ul>
+<p>We will use the LeftRightImagery paradigm. This will restrict the analysis
+to two classes (left- vs right-hand) and use AUC as metric.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Original author: Alexandre Barachant &lt;alexandre.barachant@gmail.com&gt;</span>
+<span class="c1"># Learning curve modification: Jan Sosulski</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.</p>
+<p>The CSP implementation from MNE is used. We selected 8 CSP components, as
+usually done in the literature.</p>
+<p>The Riemannian geometry pipeline consists in covariance estimation, tangent
+space mapping and finally a logistic regression for the classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lbfgs&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
+The evaluation will return a DataFrame containing a single AUC score for
+each subject / session of the dataset, and for each pipeline.</p>
+<p>Results are saved into the database, so that if you add a new pipeline, it
+will not run again the evaluation unless a parameter has changed. Results can
+be overwritten if necessary.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+<span class="c1"># Evaluate for a specific number of training samples per class</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">policy</span><span class="o">=</span><span class="s2">&quot;per_class&quot;</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">50</span><span class="p">]))</span>
+<span class="c1"># When the training data is sparse, perform more permutations than when we have a lot of data</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">floor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.geomspace.html#numpy.geomspace" title="numpy.geomspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span></a><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">])))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="p">,</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-WithinSession:   0%|          | 0/1 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-WithinSession: 100%|##########| 1/1 [00:34&lt;00:00, 34.38s/it]
+BNCI2014-001-WithinSession: 100%|##########| 1/1 [00:34&lt;00:00, 34.38s/it]
+      score      time  samples  ...  n_sessions       dataset pipeline
+0  0.704762  0.052010     10.0  ...           2  BNCI2014-001  CSP+LDA
+1  0.885714  0.057023     20.0  ...           2  BNCI2014-001  CSP+LDA
+2  1.000000  0.102005     60.0  ...           2  BNCI2014-001  CSP+LDA
+3  0.990476  0.191809    100.0  ...           2  BNCI2014-001  CSP+LDA
+4  0.480952  0.043582     10.0  ...           2  BNCI2014-001  CSP+LDA
+
+[5 rows x 11 columns]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>We plot the accuracy as a function of the number of training samples, for
+each pipeline</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">)</span>
+
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;data_size&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">results</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;data_size&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">errbar_meaning</span></a> <span class="o">=</span> <span class="s2">&quot;subjects&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">&quot;permutations&quot;</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">title_str</span></a> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Errorbar shows Mean-CI across </span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">errbar_meaning</span></a><span class="si">}</span><span class="s2">&quot;</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Amount of training samples&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">title_str</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_learning_curve_motor_imagery_001.png" srcset="../../_images/sphx_glr_plot_learning_curve_motor_imagery_001.png" alt="Errorbar shows Mean-CI across permutations" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  36.283 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  257 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/833e0bb5b511f838d653ea6df7213ca7/plot_learning_curve_motor_imagery.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_learning_curve_motor_imagery.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/5b1f506cd53838de0dd70e569a36f198/plot_learning_curve_motor_imagery.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_learning_curve_motor_imagery.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
+  
+    <div class="footer-article-item"><!-- Previous / next buttons -->
+<div class="prev-next-area">
+    <a class="left-prev"
+       href="index.html"
+       title="previous page">
+      <i class="fa-solid fa-angle-left"></i>
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Evaluation with learning curve</p>
+      </div>
+    </a>
+    <a class="right-next"
+       href="plot_learning_curve_p300.html"
+       title="next page">
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+    <li class="breadcrumb-item active" aria-current="page">Within Session P300 with Learning Curve</li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="within-session-p300-with-learning-curve">
+<span id="sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"></span><h1>Within Session P300 with Learning Curve<a class="headerlink" href="#within-session-p300-with-learning-curve" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to perform a within session analysis while also
+creating learning curves for a P300 dataset.
+Additionally, we will evaluate external code. Make sure to have tdlda installed, which
+can be found in requirements_external.txt</p>
+<p>We will compare two pipelines :</p>
+<ul class="simple">
+<li><p>Riemannian geometry with Linear Discriminant Analysis</p></li>
+<li><p>XDAWN and Linear Discriminant Analysis</p></li>
+</ul>
+<p>We will use the P300 paradigm, which uses the AUC as metric.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Jan Sosulski</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.Vectorizer.html#mne.decoding.Vectorizer" title="mne.decoding.Vectorizer" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">Vectorizer</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.spatialfilters</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Xdawn</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a>
+
+
+<span class="c1"># getting rid of the warnings about the future (on s&#39;en fout !)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+
+<a href="../../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing_sampling_rate</span></a> <span class="o">=</span> <span class="mi">128</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+</pre></div>
+</div>
+<p>We have to do this because the classes are called ‘Target’ and ‘NonTarget’
+but the evaluation function uses a LabelEncoder, transforming them
+to 0 and 1</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels_dict</span></a> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Target&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;NonTarget&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
+
+<span class="c1"># Riemannian geometry based classification</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a><span class="p">(</span><span class="n">nfilter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">,</span> <span class="n">xdawn_estimator</span><span class="o">=</span><span class="s2">&quot;scm&quot;</span><span class="p">),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;Xdw+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Xdawn</span></a><span class="p">(</span><span class="n">nfilter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;scm&quot;</span><span class="p">),</span> <a href="https://mne.tools/stable/generated/mne.decoding.Vectorizer.html#mne.decoding.Vectorizer" title="mne.decoding.Vectorizer" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">Vectorizer</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigm (P300) and use all three datasets available for it.
+The evaluation will return a DataFrame containing AUCs for each permutation
+and dataset size.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a><span class="p">(</span><span class="n">resample</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing_sampling_rate</span></a><span class="p">)</span>
+<a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a><span class="p">()</span>
+<span class="c1"># Remove the slicing of the subject list to evaluate multiple subjects</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">policy</span><span class="o">=</span><span class="s2">&quot;ratio&quot;</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.geomspace.html#numpy.geomspace" title="numpy.geomspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span></a><span class="p">(</span><span class="mf">0.02</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<span class="c1"># When the training data is sparse, perform more permutations than when we have a lot of data</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">floor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.geomspace.html#numpy.geomspace" title="numpy.geomspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span></a><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">])))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="c1"># Guarantee reproducibility</span>
+<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">7536298</span><span class="p">)</span>
+<a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data_size</span></a><span class="p">,</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_perms</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples_lr&quot;</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-009-WithinSession:   0%|          | 0/1 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_shrunk_covariance.py:327: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/covariance/_empirical_covariance.py:95: UserWarning: Only one sample available. You may want to reshape your data array
+  warnings.warn(
+
+BNCI2014-009-WithinSession: 100%|##########| 1/1 [01:05&lt;00:00, 65.35s/it]
+BNCI2014-009-WithinSession: 100%|##########| 1/1 [01:05&lt;00:00, 65.35s/it]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>We plot the accuracy as a function of the number of training samples, for
+each pipeline</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">)</span>
+
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;data_size&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">results</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;data_size&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">errbar_meaning</span></a> <span class="o">=</span> <span class="s2">&quot;subjects&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subs</span></a> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">&quot;permutations&quot;</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">title_str</span></a> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Errorbar shows Mean-CI across </span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">errbar_meaning</span></a><span class="si">}</span><span class="s2">&quot;</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Amount of training samples&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">title_str</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_learning_curve_p300_001.png" srcset="../../_images/sphx_glr_plot_learning_curve_p300_001.png" alt="Errorbar shows Mean-CI across permutations" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  7.230 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  11 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-learning-curve-plot-learning-curve-p300-py">
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diff --git a/docs/auto_examples/learning_curve/sg_execution_times.html b/docs/auto_examples/learning_curve/sg_execution_times.html
new file mode 100644
index 00000000..934f2f09
--- /dev/null
+++ b/docs/auto_examples/learning_curve/sg_execution_times.html
@@ -0,0 +1,534 @@
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+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-learning-curve-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
+<p><strong>01:43.513</strong> total execution time for <strong>auto_examples_learning_curve</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_learning_curve_p300.py</span></code>)</p></td>
+<td><p>01:07.230</p></td>
+<td><p>10.7 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_learning_curve_motor_imagery.py</span></code>)</p></td>
+<td><p>00:36.283</p></td>
+<td><p>257.3 MB</p></td>
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+<li class="toctree-l2"><a class="reference internal" href="../auto_tutorials/4_adding_a_dataset.html">Tutorial 4: Creating a dataset class</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html">Tutorial 5: Creating a dataset class</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html">Tutorial 0: Getting Started</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<section class="sphx-glr-example-title" id="load-model-scikit-pytorch-keras-with-moabb">
+<span id="sphx-glr-auto-examples-load-model-py"></span><h1>Load Model (Scikit, Pytorch, Keras) with MOABB<a class="headerlink" href="#load-model-scikit-pytorch-keras-with-moabb" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use load the pretrained pipeline in MOABB.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Igor Carrara &lt;igor.carrara@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">from</span> <span class="nn">pickle</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/pickle.html#pickle.load" title="pickle.load" class="sphx-glr-backref-module-pickle sphx-glr-backref-type-py-function"><span class="n">load</span></a>
+
+<span class="kn">import</span> <span class="nn">keras</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <span class="n">EEGClassifier</span>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <span class="n">EEGInception</span>
+<span class="kn">from</span> <span class="nn">scikeras.wrappers</span> <span class="kn">import</span> <span class="n">KerasClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EarlyStopping</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ValidSplit</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.pipelines.features</span> <span class="kn">import</span> <a href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html#moabb.pipelines.features.StandardScaler_Epoch" title="moabb.pipelines.features.StandardScaler_Epoch" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler_Epoch</span></a>
+<span class="kn">from</span> <span class="nn">moabb.utils</span> <span class="kn">import</span> <a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>In this example, we will use the results computed by the following examples</p>
+<ul class="simple">
+<li><p><a href="#id1"><span class="problematic" id="id2">plot_benchmark_</span></a></p></li>
+<li><p><a href="#id3"><span class="problematic" id="id4">plot_benchmark_braindecode_</span></a></p></li>
+<li><p><a href="#id5"><span class="problematic" id="id6">plot_benchmark_DL_</span></a></p></li>
+</ul>
+<hr class="docutils" />
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up reproducibility of Tensorflow and PyTorch</span>
+<a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Loading the Scikit-learn pipelines</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span>
+    <span class="s2">&quot;./results/Models_WithinSession/Zhou2016/1/0/CSP + SVM/fitted_model_best.pkl&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;rb&quot;</span><span class="p">,</span>
+<span class="p">)</span> <span class="k">as</span> <span class="n">pickle_file</span><span class="p">:</span>
+    <span class="n">CSP_SVM_Trained</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/pickle.html#pickle.load" title="pickle.load" class="sphx-glr-backref-module-pickle sphx-glr-backref-type-py-function"><span class="n">load</span></a><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Loading the Keras model
+We load the single Keras model, if we want we can set in the exact same pipeline.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model_Keras</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span>
+    <span class="s2">&quot;./results/Models_WithinSession/BNCI2014-001/1/1E/Keras_DeepConvNet/kerasdeepconvnet_fitted_model_best.h5&quot;</span>
+<span class="p">)</span>
+<span class="c1"># Now we need to instantiate a new SciKeras object since we only saved the Keras model</span>
+<span class="n">Keras_DeepConvNet_Trained</span> <span class="o">=</span> <span class="n">KerasClassifier</span><span class="p">(</span><span class="n">model_Keras</span><span class="p">)</span>
+<span class="c1"># Create the pipelines</span>
+
+
+<span class="n">pipes_keras</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;StandardScaler_Epoch&quot;</span><span class="p">,</span> <a href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html#moabb.pipelines.features.StandardScaler_Epoch" title="moabb.pipelines.features.StandardScaler_Epoch" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler_Epoch</span></a><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;Keras_DeepConvNet_Trained&quot;</span><span class="p">,</span> <span class="n">Keras_DeepConvNet_Trained</span><span class="p">),</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>Loading the PyTorch model</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Hyperparameter</span>
+<span class="n">LEARNING_RATE</span> <span class="o">=</span> <span class="mf">0.0001</span>
+<span class="n">WEIGHT_DECAY</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">64</span>
+<span class="n">SEED</span> <span class="o">=</span> <span class="mi">42</span>
+<span class="n">VERBOSE</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">EPOCH</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">PATIENCE</span> <span class="o">=</span> <span class="mi">3</span>
+
+<span class="c1"># Define a Skorch classifier</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">EEGClassifier</span><span class="p">(</span>
+    <span class="n">module</span><span class="o">=</span><span class="n">EEGInception</span><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><span class="n">LEARNING_RATE</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><span class="n">EPOCH</span><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ValidSplit</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">SEED</span><span class="p">),</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EarlyStopping</span></a><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="s2">&quot;valid_loss&quot;</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="n">PATIENCE</span><span class="p">),</span>
+        <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EpochScoring</span></a><span class="p">(</span>
+            <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;train_acc&quot;</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">),</span>
+        <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EpochScoring</span></a><span class="p">(</span>
+            <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;valid_acc&quot;</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">),</span>
+    <span class="p">],</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="n">VERBOSE</span><span class="p">,</span>  <span class="c1"># Not printing the results for each epoch</span>
+<span class="p">)</span>
+
+<span class="n">clf</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
+
+<span class="n">f_params</span> <span class="o">=</span> <span class="s2">&quot;./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_model.pkl&quot;</span>
+<span class="n">f_optimizer</span> <span class="o">=</span> <span class="s2">&quot;./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_optim.pkl&quot;</span>
+<span class="n">f_history</span> <span class="o">=</span> <span class="s2">&quot;./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_history.json&quot;</span>
+
+<span class="n">clf</span><span class="o">.</span><span class="n">load_params</span><span class="p">(</span><span class="n">f_params</span><span class="o">=</span><span class="n">f_params</span><span class="p">,</span> <span class="n">f_optimizer</span><span class="o">=</span><span class="n">f_optimizer</span><span class="p">,</span> <span class="n">f_history</span><span class="o">=</span><span class="n">f_history</span><span class="p">)</span>
+
+<span class="c1"># Create the pipelines</span>
+<span class="n">pipes_pytorch</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">clf</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-load-model-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">load_model.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">load_model.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+                </article>
+              
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+<div class="footer-article-items footer-article__inner">
+  
+    <div class="footer-article-item"><!-- Previous / next buttons -->
+<div class="prev-next-area">
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+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Benchmarking with MOABB showing the CO2 footprint</p>
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+       title="next page">
+      <div class="prev-next-info">
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+        <p class="prev-next-title">Convert a MOABB dataset to BIDS</p>
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new file mode 100644
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+
+<html lang="en" data-theme="light">
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+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../_static/moabb_logo.png" type="image/png">
+    <title>Convert a MOABB dataset to BIDS &#8212; moabb 1.0.0-dev documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeDataset.html">moabb.datasets.fake.FakeDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeVirtualRealityDataset.html">moabb.datasets.fake.FakeVirtualRealityDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_path.html">moabb.datasets.download.data_path</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_dl.html">moabb.datasets.download.data_dl</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_issue_request.html">moabb.datasets.download.fs_issue_request</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_list.html">moabb.datasets.download.fs_get_file_list</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_id.html">moabb.datasets.download.fs_get_file_id</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_name.html">moabb.datasets.download.fs_get_file_name</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Convert a MOABB dataset to BIDS</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
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+  
+</div>
+</div>
+              
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+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-noplot-bids-conversion-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="convert-a-moabb-dataset-to-bids">
+<span id="sphx-glr-auto-examples-noplot-bids-conversion-py"></span><h1>Convert a MOABB dataset to BIDS<a class="headerlink" href="#convert-a-moabb-dataset-to-bids" title="Permalink to this heading">#</a></h1>
+<p>The Brain Imaging Data Structure (BIDS) format
+is standard for storing neuroimaging data.
+It follows fixed principles to facilitate the
+sharing of neuroimaging data between researchers.</p>
+<p>The MOABB library allows to convert any MOABB dataset to
+BIDS <a class="footnote-reference brackets" href="#id4" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+<p>In this example, we will convert the AlexMI dataset to BIDS using the
+option <code class="docutils literal notranslate"><span class="pre">cache_config=dict(path=temp_dir,</span> <span class="pre">save_raw=True)</span></code> of the <code class="docutils literal notranslate"><span class="pre">get_data</span></code>
+method from the dataset object.</p>
+<p>This will automatically save the raw data in the BIDS format and allow to use
+a cache for the next time the dataset is used.</p>
+<p>We will use the AlexMI dataset <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>, one of the smallest in
+people and one that can be downloaded quickly.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pierre Guetschel &lt;pierre.guetschel@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">shutil</span>
+<span class="kn">import</span> <span class="nn">tempfile</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Path</span></a>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AlexMI</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="basic-usage">
+<h2>Basic usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading">#</a></h2>
+<p>Here, we will save the BIDS version of the dataset in a temporary folder</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">temp_dir</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Path</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">())</span>
+<span class="c1"># The conversion of any MOABB dataset to a BIDS-compliant structure can be done</span>
+<span class="c1"># by simply calling its ``get_data`` method and using the ``cache_config``</span>
+<span class="c1"># parameter. This parameter is a dictionary.</span>
+<span class="n">dataset</span> <span class="o">=</span> <a href="../generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AlexMI</span></a><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">cache_config</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">temp_dir</span><span class="p">,</span> <span class="n">save_raw</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+</section>
+<section id="before-after-folder-structure">
+<h2>Before / after folder structure<a class="headerlink" href="#before-after-folder-structure" title="Permalink to this heading">#</a></h2>
+<p>To investigate what was saved, we will first define a function to print
+the folder structure of a given path:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">print_tree</span><span class="p">(</span><span class="n">p</span><span class="p">:</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Path</span></a><span class="p">,</span> <span class="n">last</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">):</span>
+    <span class="n">elbow</span> <span class="o">=</span> <span class="s2">&quot;└──&quot;</span>
+    <span class="n">pipe</span> <span class="o">=</span> <span class="s2">&quot;│  &quot;</span>
+    <span class="n">tee</span> <span class="o">=</span> <span class="s2">&quot;├──&quot;</span>
+    <span class="n">blank</span> <span class="o">=</span> <span class="s2">&quot;   &quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">header</span> <span class="o">+</span> <span class="p">(</span><span class="n">elbow</span> <span class="k">if</span> <span class="n">last</span> <span class="k">else</span> <span class="n">tee</span><span class="p">)</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">is_dir</span><span class="p">():</span>
+        <span class="n">children</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">iterdir</span><span class="p">())</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">children</span><span class="p">):</span>
+            <span class="n">print_tree</span><span class="p">(</span>
+                <span class="n">c</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="n">header</span> <span class="o">+</span> <span class="p">(</span><span class="n">blank</span> <span class="k">if</span> <span class="n">last</span> <span class="k">else</span> <span class="n">pipe</span><span class="p">),</span> <span class="n">last</span><span class="o">=</span><span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">children</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+            <span class="p">)</span>
+</pre></div>
+</div>
+<p>Now, we will retrieve the location of the original dataset. It is stored
+in the MNE data directory, which can be found with the <code class="docutils literal notranslate"><span class="pre">&quot;MNE_DATA&quot;</span></code> key:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mne_data</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Path</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.get_config.html#mne.get_config" title="mne.get_config" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">get_config</span></a><span class="p">(</span><span class="s2">&quot;MNE_DATA&quot;</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;MNE data directory: </span><span class="si">{</span><span class="n">mne_data</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Now, we can print the folder structure of the original dataset:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Before conversion:&quot;</span><span class="p">)</span>
+<span class="n">print_tree</span><span class="p">(</span><span class="n">mne_data</span> <span class="o">/</span> <span class="s2">&quot;MNE-alexeeg-data&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>As we can see, before conversion, all the data (i.e. from all subjects,
+sessions and runs) is stored in a single folder. This follows no particular
+standard and can vary from one dataset to another.</p>
+<p>After conversion, the data is stored in a BIDS-compliant way:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;After conversion:&quot;</span><span class="p">)</span>
+<span class="n">print_tree</span><span class="p">(</span><span class="n">temp_dir</span> <span class="o">/</span> <span class="s2">&quot;MNE-BIDS-alexandre-motor-imagery&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>In the BIDS version of our dataset, the raw files are saved in EDF.
+The data is organized in a hierarchy of folders,
+starting with the subjects, then the sessions, and then the runs. Metadata
+files are stored to describe the data. For more details on the BIDS
+structure, please refer to the <a class="reference external" href="https://bids.neuroimaging.io">BIDS website</a>
+and the <a class="reference external" href="https://bids-specification.readthedocs.io/en/stable/">BIDS spec</a>.</p>
+<p>Under the hood, saving datasets to BIDS is done through the caching system
+of MOABB. Only raw EEG files are officially supported by the BIDS
+specification.
+However, MOABB’s caching mechanism also offers the possibility to save
+the data in a pseudo-BIDS after different preprocessing steps.
+In particular, we can save <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a> and <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code> objects.
+For more details on the caching system,
+please refer to the tutorial <a class="reference internal" href="plot_disk_cache.html"><span class="doc">Cache on disk intermediate data processing states</span></a>.</p>
+</section>
+<section id="cleanup">
+<h2>Cleanup<a class="headerlink" href="#cleanup" title="Permalink to this heading">#</a></h2>
+<p>Finally, we can delete the temporary folder:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/shutil.html#shutil.rmtree" title="shutil.rmtree" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span></a><span class="p">(</span><span class="n">temp_dir</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id4" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Pernet, C.R., Appelhoff, S., Gorgolewski, K.J. et al. EEG-BIDS,
+An extension to the brain imaging data structure for
+electroencephalography. Sci Data 6, 103 (2019).
+<a class="reference external" href="https://doi.org/10.1038/s41597-019-0104-8">https://doi.org/10.1038/s41597-019-0104-8</a></p>
+</aside>
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Appelhoff et al., (2019). MNE-BIDS: Organizing electrophysiological
+data into the BIDS format and facilitating their analysis.
+Journal of Open Source Software, 4(44), 1896,
+<a class="reference external" href="https://doi.org/10.21105/joss.01896">https://doi.org/10.21105/joss.01896</a></p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Barachant, A., 2012. Commande robuste d’un effecteur par une
+interface cerveau machine EEG asynchrone (Doctoral dissertation,
+Université de Grenoble).
+<a class="reference external" href="https://tel.archives-ouvertes.fr/tel-01196752">https://tel.archives-ouvertes.fr/tel-01196752</a></p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-noplot-bids-conversion-py">
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+<p><a class="reference download internal" download="" href="../_downloads/9ab89018120aed26b30f3efdfdef47fe/noplot_bids_conversion.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">noplot_bids_conversion.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/1e19389137690794e1345c2b750ca301/noplot_bids_conversion.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">noplot_bids_conversion.ipynb</span></code></a></p>
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+    <li class="breadcrumb-item active" aria-current="page">Spectral analysis of the trials</li>
+  </ul>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-noplot-phmd-ml-spectrum-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="spectral-analysis-of-the-trials">
+<span id="sphx-glr-auto-examples-noplot-phmd-ml-spectrum-py"></span><h1>Spectral analysis of the trials<a class="headerlink" href="#spectral-analysis-of-the-trials" title="Permalink to this heading">#</a></h1>
+<p>This example demonstrates how to perform spectral
+analysis on epochs extracted from a specific subject
+within the <a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.Cattan2019_PHMD</span></code></a>  dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro Rodrigues &lt;pedro.rodrigues01@gmail.com&gt;</span>
+<span class="c1"># Modified by: Gregoire Cattan &lt;gcattan@hotmail.fr&gt;</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Cattan2019_PHMD</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <span class="n">RestingStateToP300Adapter</span>
+
+
+<a href="https://docs.python.org/3/library/warnings.html#warnings.filterwarnings" title="warnings.filterwarnings" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span></a><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="initialization">
+<h2>Initialization<a class="headerlink" href="#initialization" title="Permalink to this heading">#</a></h2>
+<ol class="arabic simple">
+<li><p>Specify the channel and subject to compute the power spectrum.</p></li>
+<li><p>Create an instance of the <a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.Cattan2019_PHMD</span></code></a> dataset.</p></li>
+<li><p>Create an instance of the <code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.RestingStateToP300Adapter</span></code>  paradigm.
+By default, the data is filtered between 1-35 Hz,
+and epochs are extracted from 10 to 50 seconds after event tagging.</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Select channel and subject for the remaining of the example.</span>
+<span class="n">channel</span> <span class="o">=</span> <span class="s2">&quot;Cz&quot;</span>
+<span class="n">subject</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="n">dataset</span> <span class="o">=</span> <a href="../generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Cattan2019_PHMD</span></a><span class="p">()</span>
+<span class="n">events</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;on&quot;</span><span class="p">,</span> <span class="s2">&quot;off&quot;</span><span class="p">]</span>
+<span class="n">paradigm</span> <span class="o">=</span> <span class="n">RestingStateToP300Adapter</span><span class="p">(</span><span class="n">events</span><span class="o">=</span><span class="n">events</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="p">[</span><span class="n">channel</span><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="estimate-power-spectral-density">
+<h2>Estimate Power Spectral Density<a class="headerlink" href="#estimate-power-spectral-density" title="Permalink to this heading">#</a></h2>
+<ol class="arabic simple">
+<li><p>Obtain the epochs for the specified subject.</p></li>
+<li><p>Use Welch’s method to estimate the power spectral density.</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">paradigm</span><span class="o">.</span><span class="n">psd</span><span class="p">(</span><span class="n">subject</span><span class="p">,</span> <span class="n">dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="display-of-the-data">
+<h2>Display of the data<a class="headerlink" href="#display-of-the-data" title="Permalink to this heading">#</a></h2>
+<p>Plot the averaged Power Spectral Density (PSD) for each label condition,
+using the selected channel specified at the beginning of the script.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">8.2</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">condition</span> <span class="ow">in</span> <span class="n">events</span><span class="p">:</span>
+    <span class="n">mean_power</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">S</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">condition</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.log10.html#numpy.log10" title="numpy.log10" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">log10</span></a><span class="p">(</span><span class="n">mean_power</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="n">condition</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">paradigm</span><span class="o">.</span><span class="n">fmin</span><span class="p">,</span> <span class="n">paradigm</span><span class="o">.</span><span class="n">fmax</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">135</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Spectrum Magnitude (dB)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Frequency (Hz)&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;PSD for Channel &quot;</span> <span class="o">+</span> <span class="n">channel</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-noplot-phmd-ml-spectrum-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/b766ff8fcb1665a3edb7ffee945fcb2d/noplot_phmd_ml_spectrum.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">noplot_phmd_ml_spectrum.py</span></code></a></p>
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+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/0621540f2a81072e792d3fe5436855e1/noplot_phmd_ml_spectrum.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">noplot_phmd_ml_spectrum.ipynb</span></code></a></p>
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+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Changing epoch size in P300 VR dataset</a></li>
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+
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_DL.html">Benchmarking on MOABB with Tensorflow deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_braindecode.html">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_grid_search.html">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_subject_ssvep.html">Cross-Subject SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_disk_cache.html">Cache on disk intermediate data processing states</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_p300.html">Within Session P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_ssvep.html">Within Session SSVEP</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.BI2015b.html">moabb.datasets.BI2015b</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_VR.html">moabb.datasets.Cattan2019_VR</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.BNCI2014_008.html">moabb.datasets.BNCI2014_008</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.DemonsP300.html">moabb.datasets.DemonsP300</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Huebner2017.html">moabb.datasets.Huebner2017</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Sosulski2019.html">moabb.datasets.Sosulski2019</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Wang2016.html">moabb.datasets.Wang2016</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM2.html">moabb.datasets.MAMEM2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM3.html">moabb.datasets.MAMEM3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_SSVEP.html">moabb.datasets.Lee2019_SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2015.html">moabb.datasets.Thielen2015</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2021.html">moabb.datasets.Thielen2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP100.html">moabb.datasets.CastillosBurstVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Changing epoch size in P300 VR dataset</li>
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+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-noplot-vr-pc-p300-different-epoch-size-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<p class="sphx-glr-example-title" id="sphx-glr-auto-examples-noplot-vr-pc-p300-different-epoch-size-py">Example of P300 classification with different epoch size.</p>
+<section id="changing-epoch-size-in-p300-vr-dataset">
+<h1>Changing epoch size in P300 VR dataset<a class="headerlink" href="#changing-epoch-size-in-p300-vr-dataset" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to extract the epochs from the P300-VR dataset of a given
+subject and then classify them using Riemannian Geometry framework for BCI.
+We compare the scores in the VR and PC conditions, using different epoch size.</p>
+<p>This example demonstrates the use of <cite>get_block_repetition</cite>, which allows
+to specify the experimental blocks and repetitions for analysis.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro Rodrigues &lt;pedro.rodrigues01@gmail.com&gt;</span>
+<span class="c1"># Modified by: Gregoire Cattan &lt;gcattan@hotmail.fr&gt;</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyriemann.classification</span> <span class="kn">import</span> <span class="n">MDM</span>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <span class="n">ERPCovariances</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">roc_auc_score</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KFold</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LabelEncoder</span></a>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Cattan2019_VR</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">P300</span></a>
+
+
+<a href="https://docs.python.org/3/library/warnings.html#warnings.filterwarnings" title="warnings.filterwarnings" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span></a><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="initialization">
+<h2>Initialization<a class="headerlink" href="#initialization" title="Permalink to this heading">#</a></h2>
+<ol class="arabic simple">
+<li><p>Create an instance of the dataset.</p></li>
+<li><p>Create an instance of a P300 paradigm.
+By default filtering between 1-24 Hz
+with epochs of length 1s.
+In this example we will be modifying the length of the epochs, by
+changing the <cite>tmax</cite> attribute of the paradigm.</p></li>
+<li><p>Encode categorical variable (Target/NonTarget) to numerical values.
+We will be using label encoding.</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <a href="../generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Cattan2019_VR</span></a><span class="p">()</span>
+<span class="n">paradigm</span> <span class="o">=</span> <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">P300</span></a><span class="p">()</span>
+<span class="n">le</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LabelEncoder</span></a><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">([</span><span class="s2">&quot;Target&quot;</span><span class="p">,</span> <span class="s2">&quot;NonTarget&quot;</span><span class="p">])</span>
+
+<span class="c1"># change this to include more subjects</span>
+<span class="n">nsubjects</span> <span class="o">=</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</section>
+<section id="validation">
+<h2>Validation<a class="headerlink" href="#validation" title="Permalink to this heading">#</a></h2>
+<p>We will perform a 3-folds validation for each combination of
+tmax, subjects and experimental conditions (VR or PC).</p>
+<p>Not all the data will be used for this validation.
+The Cattan2019_VR dataset contains the data from a randomized experiment.
+We will only be using the two first repetitions of the 12 experimental blocks.
+Data will be selected thanks to the <cite>get_block_repetition</cite> method.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Contains the score for all combination of tmax, subjects</span>
+<span class="c1"># and experimental condition (VR or PC).</span>
+<span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
+
+<span class="c1"># Init 3-folds validation.</span>
+<span class="n">kf</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+
+<span class="c1"># Select the first two repetitions.</span>
+<span class="n">repetitions</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+
+<span class="c1"># Generate all possible arrangement with the 12 blocks.</span>
+<span class="n">blocks</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">12</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># run validation for each combination.</span>
+<span class="k">for</span> <span class="n">tmax</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]:</span>
+    <span class="n">paradigm</span><span class="o">.</span><span class="n">tmax</span> <span class="o">=</span> <span class="n">tmax</span>
+
+    <span class="k">for</span> <span class="n">subject</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span><span class="p">[:</span><span class="n">nsubjects</span><span class="p">]):</span>
+        <span class="c1"># Note: here we are adding `tmax` to scores_subject,</span>
+        <span class="c1"># although `tmax` is defined outside the scope of this inner loop.</span>
+        <span class="c1"># The reason behind is to facilitate the conversion from array to dataframe at the end.</span>
+        <span class="n">scores_subject</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmax</span><span class="p">,</span> <span class="n">subject</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">condition</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;VR&quot;</span><span class="p">,</span> <span class="s2">&quot;PC&quot;</span><span class="p">]:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;subject </span><span class="si">{</span><span class="n">subject</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">condition</span><span class="si">}</span><span class="s2">, tmax </span><span class="si">{</span><span class="n">tmax</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="c1"># Rather than creating a new instance depending on the condition,</span>
+            <span class="c1"># let&#39;s change the attribute value to download the correct data.</span>
+            <span class="n">dataset</span><span class="o">.</span><span class="n">virtual_reality</span> <span class="o">=</span> <span class="n">condition</span> <span class="o">==</span> <span class="s2">&quot;VR&quot;</span>
+            <span class="n">dataset</span><span class="o">.</span><span class="n">personal_computer</span> <span class="o">=</span> <span class="n">condition</span> <span class="o">==</span> <span class="s2">&quot;PC&quot;</span>
+
+            <span class="n">auc</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="c1"># Split in training and testing blocks, and fit/predict.</span>
+            <span class="c1"># This loop will run 3 times as we are using a 3-folds validation</span>
+            <span class="k">for</span> <span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span> <span class="ow">in</span> <span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">12</span><span class="p">)):</span>
+                <span class="c1"># Note the use of the `get_block_repetition` method,</span>
+                <span class="c1"># to select the appropriate number of blocks and repetitions:</span>
+                <span class="c1"># - 8 blocks for training, 4 for testing</span>
+                <span class="c1"># - only the first two repetitions inside each blocks</span>
+                <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_block_repetition</span><span class="p">(</span>
+                    <span class="n">paradigm</span><span class="p">,</span> <span class="p">[</span><span class="n">subject</span><span class="p">],</span> <span class="n">blocks</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">repetitions</span>
+                <span class="p">)</span>
+
+                <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_block_repetition</span><span class="p">(</span>
+                    <span class="n">paradigm</span><span class="p">,</span> <span class="p">[</span><span class="n">subject</span><span class="p">],</span> <span class="n">blocks</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">repetitions</span>
+                <span class="p">)</span>
+
+                <span class="c1"># We use riemannian geometry processing techniques with MDM algorithm.</span>
+                <span class="n">pipe</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">ERPCovariances</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">),</span> <span class="n">MDM</span><span class="p">())</span>
+                <span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+                <span class="n">y_pred</span> <span class="o">=</span> <span class="n">pipe</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+                <span class="c1"># y_test and y_pred contains categorical variable (Target/NonTarget).</span>
+                <span class="c1"># To use a metric, we need to convert target information to numerical values.</span>
+                <span class="n">y_test</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>
+                <span class="n">y_pred</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span>
+
+                <span class="c1"># We use the roc_auc_score, which is a reliable metric for multi-class problem.</span>
+                <span class="n">auc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">roc_auc_score</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
+
+            <span class="c1"># stock scores</span>
+            <span class="n">scores_subject</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">auc</span><span class="p">))</span>
+
+        <span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scores_subject</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="display-of-the-data">
+<h2>Display of the data<a class="headerlink" href="#display-of-the-data" title="Permalink to this heading">#</a></h2>
+<p>Let’s transform or array to a dataframe.
+We can then print it on the console, and
+plot the mean AUC as a function of the epoch length.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;tmax&quot;</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;VR&quot;</span><span class="p">,</span> <span class="s2">&quot;PC&quot;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;tmax&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;VR&quot;</span><span class="p">,</span> <span class="s2">&quot;PC&quot;</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Mean AUC as a function of the epoch length&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-noplot-vr-pc-p300-different-epoch-size-py">
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+<p><a class="reference download internal" download="" href="../_downloads/5303bff802560c798e898bab8eb895a3/noplot_vr_pc_p300_different_epoch_size.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">noplot_vr_pc_p300_different_epoch_size.py</span></code></a></p>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#initialization">Initialization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#validation">Validation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#display-of-the-data">Display of the data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2021.html">moabb.datasets.Thielen2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
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+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-benchmark-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="examples-of-how-to-use-moabb-to-benchmark-pipelines">
+<span id="sphx-glr-auto-examples-plot-benchmark-py"></span><h1>Examples of how to use MOABB to benchmark pipelines.<a class="headerlink" href="#examples-of-how-to-use-moabb-to-benchmark-pipelines" title="Permalink to this heading">#</a></h1>
+</section>
+<section id="benchmarking-with-moabb">
+<h1>Benchmarking with MOABB<a class="headerlink" href="#benchmarking-with-moabb" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use MOABB to benchmark a set of pipelines
+on all available datasets. For this example, we will use only one
+dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Sylvain Chevallier &lt;sylvain.chevallier@universite-paris-saclay.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">,</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.analysis.plotting</span> <span class="kn">import</span> <a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="loading-the-pipelines">
+<h2>Loading the pipelines<a class="headerlink" href="#loading-the-pipelines" title="Permalink to this heading">#</a></h2>
+<p>The ML pipelines used in benchmark are defined in YAML files, following a
+simple format. It simplifies sharing and reusing pipelines across benchmarks,
+reproducing state-of-the-art results.</p>
+<p>MOABB comes with complete list of pipelines that cover most of the successful
+approaches in the literature. You can find them in the
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/tree/develop/pipelines">pipelines folder</a>.
+For this example, we will use a folder with only 2 pipelines, to keep the
+computation time low.</p>
+<p>This is an example of a pipeline defined in YAML, defining on which paradigms it
+can be used, the original publication, and the steps to perform using a
+scikit-learn API. In this case, a CSP + SVM pipeline, the covariance are estimated
+to compute a CSP filter and then a linear SVM is trained on the CSP filtered
+signals.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;sample_pipelines/CSP_SVM.yml&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lines</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+    <span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">line</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lines</span></a><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">line</span></a><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name: CSP + SVM
+paradigms:
+  - LeftRightImagery
+
+citations:
+  - https://doi.org/10.1007/BF01129656
+  - https://doi.org/10.1109/MSP.2008.4408441
+
+pipeline:
+  - name: Covariances
+    from: pyriemann.estimation
+    parameters:
+      estimator: oas
+
+  - name: CSP
+    from: pyriemann.spatialfilters
+    parameters:
+      nfilter: 6
+
+  - name: SVC
+    from: sklearn.svm
+    parameters:
+      kernel: &quot;linear&quot;
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">sample_pipelines</span></code> folder contains a second pipeline, a logistic regression
+performed in the tangent space using Riemannian geometry.</p>
+</section>
+<section id="selecting-the-datasets-optional">
+<h2>Selecting the datasets (optional)<a class="headerlink" href="#selecting-the-datasets-optional" title="Permalink to this heading">#</a></h2>
+<p>If you want to limit your benchmark on a subset of datasets, you can use the
+<code class="docutils literal notranslate"><span class="pre">include_datasets</span></code> and <code class="docutils literal notranslate"><span class="pre">exclude_datasets</span></code> arguments. You will need either
+to provide the dataset’s object, or a the dataset’s code. To get the list of
+available dataset’s code for a given paradigm, you can use the following command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<span class="k">for</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a> <span class="ow">in</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" title="moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" class="sphx-glr-backref-module-moabb-paradigms-motor_imagery sphx-glr-backref-type-py-property"><span class="n">paradigm</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span><span class="o">.</span><span class="n">code</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/fake.py:93: RuntimeWarning: Setting non-standard config type: &quot;MNE_DATASETS_FAKEDATASET-IMAGERY-10-2--60-60--120-120--FAKE1-FAKE2-FAKE3--C3-CZ-C4_PATH&quot;
+  set_config(key, temp_dir)
+/home/runner/work/moabb/moabb/moabb/datasets/fake.py:93: RuntimeWarning: Setting non-standard config type: &quot;MNE_DATASETS_FAKEVIRTUALREALITYDATASET-P300-21-1--60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60--120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120--TARGET-NONTARGET--C3-CZ-C4_PATH&quot;
+  set_config(key, temp_dir)
+BNCI2014-001
+BNCI2014-004
+Cho2017
+GrosseWentrup2009
+Lee2019-MI
+PhysionetMotorImagery
+Schirrmeister2017
+Shin2017A
+Weibo2014
+Zhou2016
+</pre></div>
+</div>
+<p>In this example, we will use only the last dataset, ‘Zhou 2016’.</p>
+</section>
+<section id="running-the-benchmark">
+<h2>Running the benchmark<a class="headerlink" href="#running-the-benchmark" title="Permalink to this heading">#</a></h2>
+<p>The benchmark is run using the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> function. You need to specify the
+folder containing the pipelines to use, the kind of evaluation and the paradigm
+to use. By default, the benchmark will use all available datasets for all
+paradigms listed in the pipelines. You could restrict to specific evaluation and
+paradigm using the <code class="docutils literal notranslate"><span class="pre">evaluations</span></code> and <code class="docutils literal notranslate"><span class="pre">paradigms</span></code> arguments.</p>
+<p>To save computation time, the results are cached. If you want to re-run the
+benchmark, you can set the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<p>It is possible to indicate the folder to cache the results and the one to save
+the analysis &amp; figures. By default, the results are saved in the <code class="docutils literal notranslate"><span class="pre">results</span></code>
+folder, and the analysis &amp; figures are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">(</span>
+    <span class="n">pipelines</span><span class="o">=</span><span class="s2">&quot;./sample_pipelines/&quot;</span><span class="p">,</span>
+    <span class="n">evaluations</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;WithinSession&quot;</span><span class="p">],</span>
+    <span class="n">paradigms</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LeftRightImagery&quot;</span><span class="p">],</span>
+    <span class="n">include_datasets</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Zhou2016&quot;</span><span class="p">],</span>
+    <span class="n">results</span><span class="o">=</span><span class="s2">&quot;./results/&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">output</span><span class="o">=</span><span class="s2">&quot;./benchmark/&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>2024-04-18 14:42:47,545 INFO MainThread moabb.evaluations.base Processing dataset: Zhou2016
+
+Zhou2016-WithinSession:   0%|          | 0/4 [00:00&lt;?, ?it/s]MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
+All datasets will be downloaded to this location, if anything is already downloaded, please move manually to this location
+/home/runner/work/moabb/moabb/moabb/datasets/download.py:55: RuntimeWarning: Setting non-standard config type: &quot;MNE_DATASETS_ZHOU_PATH&quot;
+  set_config(key, get_config(&quot;MNE_DATA&quot;))
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~8.0 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~7.9 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+2024-04-18 14:42:51,361 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 1 | 0: Score 0.860
+2024-04-18 14:42:51,863 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 1 | 0: Score 0.909
+2024-04-18 14:42:52,268 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 1 | 1: Score 0.924
+2024-04-18 14:42:52,718 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 1 | 1: Score 0.926
+2024-04-18 14:42:53,110 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 1 | 2: Score 0.954
+2024-04-18 14:42:53,553 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 1 | 2: Score 0.964
+
+Zhou2016-WithinSession:  25%|##5       | 1/4 [00:06&lt;00:18,  6.13s/it]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~5.4 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+2024-04-18 14:42:55,852 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 2 | 0: Score 0.916
+2024-04-18 14:42:56,315 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 2 | 0: Score 0.910
+2024-04-18 14:42:56,692 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 2 | 1: Score 0.753
+2024-04-18 14:42:57,123 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 2 | 1: Score 0.765
+2024-04-18 14:42:57,514 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 2 | 2: Score 0.906
+2024-04-18 14:42:57,953 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 2 | 2: Score 0.904
+
+Zhou2016-WithinSession:  50%|#####     | 2/4 [00:10&lt;00:10,  5.11s/it]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+2024-04-18 14:43:00,455 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 3 | 0: Score 0.972
+2024-04-18 14:43:00,897 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 3 | 0: Score 0.994
+2024-04-18 14:43:01,289 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 3 | 1: Score 0.980
+2024-04-18 14:43:01,739 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 3 | 1: Score 0.990
+2024-04-18 14:43:02,130 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 3 | 2: Score 0.990
+2024-04-18 14:43:02,579 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 3 | 2: Score 0.992
+
+Zhou2016-WithinSession:  75%|#######5  | 3/4 [00:15&lt;00:04,  4.89s/it]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~5.4 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+2024-04-18 14:43:04,857 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 4 | 0: Score 0.993
+2024-04-18 14:43:05,276 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 4 | 0: Score 0.995
+2024-04-18 14:43:05,676 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 4 | 1: Score 0.972
+2024-04-18 14:43:06,131 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 4 | 1: Score 0.972
+2024-04-18 14:43:06,526 INFO MainThread moabb.evaluations.base CSP + SVM | Zhou2016 | 4 | 2: Score 0.968
+2024-04-18 14:43:06,968 INFO MainThread moabb.evaluations.base Tangent Space LR | Zhou2016 | 4 | 2: Score 0.978
+
+Zhou2016-WithinSession: 100%|##########| 4/4 [00:19&lt;00:00,  4.69s/it]
+Zhou2016-WithinSession: 100%|##########| 4/4 [00:19&lt;00:00,  4.89s/it]
+    dataset     evaluation          pipeline  avg score
+0  Zhou2016  WithinSession         CSP + SVM   0.932315
+1  Zhou2016  WithinSession  Tangent Space LR   0.941601
+</pre></div>
+</div>
+<p>Benchmark prints a summary of the results. Detailed results are saved in a
+pandas dataframe, and can be used to generate figures. The analysis &amp; figures
+are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_benchmark_001.png" srcset="../_images/sphx_glr_plot_benchmark_001.png" alt="Scores per dataset and algorithm" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>2024-04-18 14:43:07,464 WARNING MainThread moabb.analysis.plotting Dataset names are too similar, turning off name shortening
+/home/runner/work/moabb/moabb/moabb/analysis/plotting.py:70: UserWarning: The palette list has more values (6) than needed (2), which may not be intended.
+  sea.stripplot(
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  20.819 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  352 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-benchmark-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/5657e54b8c692376a881117a99de998e/plot_benchmark.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_benchmark.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/225d2b2bd0960a1fa6a57be5e1838af9/plot_benchmark.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_benchmark.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
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+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Examples of how to use MOABB to benchmark pipelines.</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#benchmarking-with-moabb">Benchmarking with MOABB</a><ul class="visible nav section-nav flex-column">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#selecting-the-datasets-optional">Selecting the datasets (optional)</a></li>
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+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Benchmarking on MOABB with Tensorflow deep net architectures</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.BaseDataset.html">moabb.datasets.base.BaseDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.CacheConfig.html">moabb.datasets.base.CacheConfig</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeDataset.html">moabb.datasets.fake.FakeDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeVirtualRealityDataset.html">moabb.datasets.fake.FakeVirtualRealityDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_path.html">moabb.datasets.download.data_path</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_dl.html">moabb.datasets.download.data_dl</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_issue_request.html">moabb.datasets.download.fs_issue_request</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_list.html">moabb.datasets.download.fs_get_file_list</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_hash.html">moabb.datasets.download.fs_get_file_hash</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_id.html">moabb.datasets.download.fs_get_file_id</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_name.html">moabb.datasets.download.fs_get_file_name</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.dataset_search.html">moabb.datasets.utils.dataset_search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.find_intersecting_channels.html">moabb.datasets.utils.find_intersecting_channels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014a_Il.html">moabb.datasets.compound_dataset.BI2014a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+<div class="header-article-items header-article__inner">
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+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Benchmarking on MOABB with Tensorflow deep net architectures</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
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+  
+</div>
+</div>
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+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-on-moabb-with-tensorflow-deep-net-architectures">
+<span id="sphx-glr-auto-examples-plot-benchmark-dl-py"></span><h1>Benchmarking on MOABB with Tensorflow deep net architectures<a class="headerlink" href="#benchmarking-on-moabb-with-tensorflow-deep-net-architectures" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use MOABB to benchmark a set of Deep Learning pipeline (Tensorflow)
+on all available datasets.
+For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Igor Carrara &lt;igor.carrara@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="kn">from</span> <span class="nn">absl.logging</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ERROR</span></a><span class="p">,</span> <span class="n">set_verbosity</span>
+<span class="kn">from</span> <span class="nn">tensorflow</span> <span class="kn">import</span> <span class="n">keras</span>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">,</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.analysis.plotting</span> <span class="kn">import</span> <a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.utils</span> <span class="kn">import</span> <a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+<span class="c1"># Avoid output Warning</span>
+<span class="n">set_verbosity</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ERROR</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/os.html#os.environ" title="os.environ" class="sphx-glr-backref-module-os sphx-glr-backref-type-py-data"><span class="n">os</span><span class="o">.</span><span class="n">environ</span></a><span class="p">[</span><span class="s2">&quot;TF_CPP_MIN_LOG_LEVEL&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;3&quot;</span>
+
+<span class="c1"># Print Information Tensorflow</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Tensorflow Version: </span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Keras Version: </span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">keras</span><span class="o">.</span><span class="n">__version__</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">CPU</span></a> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">list_physical_devices</span><span class="p">(</span><span class="s2">&quot;CPU&quot;</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CPU is&quot;</span><span class="p">,</span> <span class="s2">&quot;AVAILABLE&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">CPU</span></a> <span class="k">else</span> <span class="s2">&quot;NOT AVAILABLE&quot;</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GPU</span></a> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">list_physical_devices</span><span class="p">(</span><span class="s2">&quot;GPU&quot;</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GPU is&quot;</span><span class="p">,</span> <span class="s2">&quot;AVAILABLE&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GPU</span></a> <span class="k">else</span> <span class="s2">&quot;NOT AVAILABLE&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Tensorflow Version: 2.11.1
+Keras Version: 2.11.0
+CPU is AVAILABLE
+GPU is NOT AVAILABLE
+</pre></div>
+</div>
+<p>In this example, we will use only the dataset <code class="docutils literal notranslate"><span class="pre">BNCI2014_001</span></code>.</p>
+<section id="running-the-benchmark">
+<h2>Running the benchmark<a class="headerlink" href="#running-the-benchmark" title="Permalink to this heading">#</a></h2>
+<p>The benchmark is run using the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> function. You need to specify the
+folder containing the pipelines to use, the kind of evaluation and the paradigm
+to use. By default, the benchmark will use all available datasets for all
+paradigms listed in the pipelines. You could restrict to specific evaluation and
+paradigm using the <code class="docutils literal notranslate"><span class="pre">evaluations</span></code> and <code class="docutils literal notranslate"><span class="pre">paradigms</span></code> arguments.</p>
+<p>To save computation time, the results are cached. If you want to re-run the
+benchmark, you can set the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<p>It is possible to indicate the folder to cache the results and the one to save
+the analysis &amp; figures. By default, the results are saved in the <code class="docutils literal notranslate"><span class="pre">results</span></code>
+folder, and the analysis &amp; figures are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<p>This code is implemented to run on CPU. If you’re using a GPU, do not use multithreading
+(i.e. set n_jobs=1)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up reproducibility of Tensorflow</span>
+<a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="c1"># Restrict this example only on the first two subject of BNCI2014_001</span>
+<a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">(</span>
+    <span class="n">pipelines</span><span class="o">=</span><span class="s2">&quot;./pipelines_DL&quot;</span><span class="p">,</span>
+    <span class="n">evaluations</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;WithinSession&quot;</span><span class="p">],</span>
+    <span class="n">paradigms</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LeftRightImagery&quot;</span><span class="p">],</span>
+    <span class="n">include_datasets</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <span class="n">results</span><span class="o">=</span><span class="s2">&quot;./results/&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">output</span><span class="o">=</span><span class="s2">&quot;./benchmark/&quot;</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/download.py:55: RuntimeWarning: Setting non-standard config type: &quot;MNE_DATASETS_BNCI_PATH&quot;
+  set_config(key, get_config(&quot;MNE_DATA&quot;))
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+
+BNCI2014-001-WithinSession:  50%|#####     | 1/2 [04:27&lt;04:27, 267.40s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+/home/runner/work/moabb/moabb/moabb/pipelines/features.py:160: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return X.get_data()
+
+BNCI2014-001-WithinSession: 100%|##########| 2/2 [09:01&lt;00:00, 271.58s/it]
+BNCI2014-001-WithinSession: 100%|##########| 2/2 [09:01&lt;00:00, 270.95s/it]
+        dataset     evaluation              pipeline  avg score
+0  BNCI2014-001  WithinSession        Keras_EEGITNet   0.583231
+1  BNCI2014-001  WithinSession        Keras_EEGTCNet   0.499898
+2  BNCI2014-001  WithinSession          Keras_EEGNeX   0.569677
+3  BNCI2014-001  WithinSession      Keras_EEGNet_8_2   0.517636
+4  BNCI2014-001  WithinSession     Keras_DeepConvNet   0.521548
+5  BNCI2014-001  WithinSession  Keras_ShallowConvNet   0.619575
+</pre></div>
+</div>
+<p>The deep learning architectures implemented in MOABB are:
+- Shallow Convolutional Network <a class="footnote-reference brackets" href="#id7" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>
+- Deep Convolutional Network <a class="footnote-reference brackets" href="#id7" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>
+- EEGNet <a class="footnote-reference brackets" href="#id8" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>
+- EEGTCNet <a class="footnote-reference brackets" href="#id9" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>
+- EEGNex <a class="footnote-reference brackets" href="#id10" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>
+- EEGITNet <a class="footnote-reference brackets" href="#id11" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a></p>
+<p>Benchmark prints a summary of the results. Detailed results are saved in a
+pandas dataframe, and can be used to generate figures. The analysis &amp; figures
+are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_benchmark_DL_001.png" srcset="../_images/sphx_glr_plot_benchmark_DL_001.png" alt="Scores per dataset and algorithm" class = "sphx-glr-single-img"/></section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>)</span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+Glasstetter, M., Eggensperger, K., Tangermann, M., … &amp; Ball, T. (2017).
+<a class="reference external" href="https://doi.org/10.1002/hbm.23730">Deep learning with convolutional neural networks for EEG decoding and
+visualization</a>.
+Human brain mapping, 38(11), 5391-5420.</p>
+</aside>
+<aside class="footnote brackets" id="id8" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,
+Hung, C. P., &amp; Lance, B. J. (2018). <a class="reference external" href="https://doi.org/10.1088/1741-2552/aace8c">EEGNet: a compact convolutional neural
+network for EEG-based brain-computer interfaces.</a>
+Journal of neural engineering, 15(5), 056013.</p>
+</aside>
+<aside class="footnote brackets" id="id9" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">3</a><span class="fn-bracket">]</span></span>
+<p>Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., &amp;
+Benini, L. (2020, October). <a class="reference external" href="https://doi.org/10.1109/SMC42975.2020.9283028">EEG-TCNet: An accurate temporal convolutional
+network for embedded motor-imagery brain-machine interfaces.</a>
+In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
+(pp. 2958-2965). IEEE.</p>
+</aside>
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">4</a><span class="fn-bracket">]</span></span>
+<p>Chen, X., Teng, X., Chen, H., Pan, Y., &amp; Geyer, P. (2022). <a class="reference external" href="https://doi.org/10.48550/arXiv.2207.12369">Toward reliable
+signals decoding for electroencephalogram: A benchmark study to EEGNeX.</a>
+arXiv preprint arXiv:2207.12369.</p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id6">5</a><span class="fn-bracket">]</span></span>
+<p>Salami, A., Andreu-Perez, J., &amp; Gillmeister, H. (2022). <a class="reference external" href="https://doi.org/10.1109/ACCESS.2022.3161489">EEG-ITNet: An
+explainable inception temporal convolutional network for motor imagery
+classification</a>.
+IEEE Access, 10, 36672-36685.</p>
+</aside>
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+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark.html">Examples of how to use MOABB to benchmark pipelines.</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_DL.html">Benchmarking on MOABB with Tensorflow deep net architectures</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_subject_ssvep.html">Cross-Subject SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_disk_cache.html">Cache on disk intermediate data processing states</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_fixed_interval_windows.html">Fixed interval windows processing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_p300.html">Within Session P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_ssvep.html">Within Session SSVEP</a></li>
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+      <a href="../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../overview.html" class="nav-link">Documentation overview</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-on-moabb-with-braindecode-pytorch-deep-net-architectures">
+<span id="sphx-glr-auto-examples-plot-benchmark-braindecode-py"></span><h1>Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures<a class="headerlink" href="#benchmarking-on-moabb-with-braindecode-pytorch-deep-net-architectures" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use MOABB to benchmark a set of Braindecode pipelines (deep learning
+architectures) on all available datasets.
+For this example, we will use only 2 datasets to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Igor Carrara &lt;igor.carrara@inria.fr&gt;</span>
+<span class="c1">#          Bruno Aristimunha &lt;b.aristimunha@gmail.com&gt;</span>
+<span class="c1">#          Sylvain Chevallier &lt;sylvain.chevallier@universite-paris-saclay.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">absl.logging</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ERROR</span></a><span class="p">,</span> <span class="n">set_verbosity</span>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">,</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.analysis.plotting</span> <span class="kn">import</span> <a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <a href="../generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_004</span></a>
+<span class="kn">from</span> <span class="nn">moabb.utils</span> <span class="kn">import</span> <a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+<span class="c1"># Avoid output Warning</span>
+<span class="n">set_verbosity</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ERROR</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/os.html#os.environ" title="os.environ" class="sphx-glr-backref-module-os sphx-glr-backref-type-py-data"><span class="n">os</span><span class="o">.</span><span class="n">environ</span></a><span class="p">[</span><span class="s2">&quot;TF_CPP_MIN_LOG_LEVEL&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;3&quot;</span>
+
+<span class="c1"># Print Information PyTorch</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Torch Version: </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="c1"># Set up GPU if it is there</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GPU is&quot;</span><span class="p">,</span> <span class="s2">&quot;AVAILABLE&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;NOT AVAILABLE&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Torch Version: 1.13.1+cu117
+GPU is NOT AVAILABLE
+</pre></div>
+</div>
+<p>In this example, we will use only 2 subjects from the dataset <code class="docutils literal notranslate"><span class="pre">BNCI2014_001</span></code> and <code class="docutils literal notranslate"><span class="pre">BNCI2014_004</span></code>.</p>
+<section id="running-the-benchmark">
+<h2>Running the benchmark<a class="headerlink" href="#running-the-benchmark" title="Permalink to this heading">#</a></h2>
+<p>The benchmark is run using the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> function. You need to specify the
+folder containing the pipelines, the kind of evaluation, and the paradigm
+to use. By default, the benchmark will use all available datasets for all
+paradigms listed in the pipelines. You could restrict to specific evaluation and
+paradigm using the <code class="docutils literal notranslate"><span class="pre">evaluations</span></code> and <code class="docutils literal notranslate"><span class="pre">paradigms</span></code> arguments.</p>
+<p>To save computation time, the results are cached. If you want to re-run the
+benchmark, you can set the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<p>It is possible to indicate the folder to cache the results and the one to save
+the analysis &amp; figures. By default, the results are saved in the <code class="docutils literal notranslate"><span class="pre">results</span></code>
+folder, and the analysis &amp; figures are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<p>This code is implemented to run on CPU. If you’re using a GPU, do not use multithreading
+(i.e. set n_jobs=1)</p>
+<p>In order to allow the benchmark function to work with return_epoch=True (Required to use Braindecode(
+we need to call each pipeline as “braindecode_xxx…”, with xxx the name of the model to be
+handled correctly by the benchmark function.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up reproducibility of Tensorflow</span>
+<a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="c1"># Restrict this example only to the first two subjects of BNCI2014_001</span>
+<a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="../generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset2</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_004</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset2</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset2</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="../generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset2</span></a><span class="p">]</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">(</span>
+    <span class="n">pipelines</span><span class="o">=</span><span class="s2">&quot;./pipelines_braindecode&quot;</span><span class="p">,</span>
+    <span class="n">evaluations</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;CrossSession&quot;</span><span class="p">],</span>
+    <span class="n">paradigms</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LeftRightImagery&quot;</span><span class="p">],</span>
+    <span class="n">include_datasets</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span>
+    <span class="n">results</span><span class="o">=</span><span class="s2">&quot;./results/&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">output</span><span class="o">=</span><span class="s2">&quot;./benchmark/&quot;</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5625        0.7963       0.4828        0.7777  0.3289
+      2       0.5156        0.9597       0.4828        0.7741  0.2913
+      3       0.4844        0.8870       0.4828        0.7706  0.2847
+      4       0.5625        0.7234       0.4828        0.7670  0.2986
+      5       0.4531        0.9164       0.4828        0.7637  0.2815
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5312        0.9508       0.4483        0.8963  0.2877
+      2       0.4062        0.9827       0.4828        0.8552  0.2886
+      3       0.4531        0.8892       0.4483        0.8198  0.2824
+      4       0.5000        0.8186       0.4828        0.7930  0.2816
+      5       0.5781        0.7252       0.5172        0.7710  0.2898
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Using padding=&#39;same&#39; with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at ../aten/src/ATen/native/Convolution.cpp:895.)
+  return F.conv2d(input, weight, bias, self.stride,
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5938        0.9143       0.4483        0.6988  0.6026
+      2       0.4219        1.0801       0.4483        0.6990  0.5695
+      3       0.4688        1.1433       0.4483        0.6992  0.5854
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4375        1.0006       0.5862        0.6848  0.5691
+      2       0.5625        0.9168       0.5862        0.6847  0.5687
+      3       0.4375        0.9807       0.5862        0.6847  0.5680
+      4       0.5000        0.9120       0.5517        0.6848  0.5803
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5312        0.6886       0.4828        0.6959  0.0957
+      2       0.5312        0.6834       0.4828        0.6959  0.0929
+      3       0.4688        0.7498       0.4828        0.6959  0.0904
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5312        0.7524       0.5172        0.6968  0.0911
+      2       0.4844        0.7341       0.5172        0.6968  0.0901
+      3       0.5625        0.6984       0.5517        0.6967  0.1030
+      4       0.4688        0.7200       0.5517        0.6967  0.0900
+      5       0.3750        0.7751       0.5517        0.6967  0.0897
+Stopping since valid_loss has not improved in the last 3 epochs.
+
+BNCI2014-001-CrossSession:  50%|#####     | 1/2 [00:16&lt;00:16, 16.66s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4844        1.1032       0.4483        0.8907  0.2792
+      2       0.5156        1.2419       0.4483        0.9051  0.2806
+      3       0.4062        1.2040       0.4828        0.9284  0.2770
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4844        0.9918       0.5517        0.7643  0.2766
+      2       0.5156        1.0123       0.5517        0.7699  0.2815
+      3       0.5312        0.8275       0.5517        0.7746  0.2780
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.3906        0.9882       0.4828        0.7055  0.5708
+      2       0.4219        1.0244       0.4483        0.7052  0.5534
+      3       0.4531        0.8173       0.4483        0.7051  0.5517
+      4       0.6719        0.7267       0.4483        0.7052  0.5526
+      5       0.5156        0.7883       0.4483        0.7051  0.5652
+      6       0.5000        0.8663       0.4483        0.7050  0.5687
+      7       0.6250        0.7473       0.4483        0.7050  0.5671
+      8       0.4062        1.0788       0.4483        0.7049  0.5506
+      9       0.6250        0.7372       0.4483        0.7047  0.5509
+     10       0.6406        0.7057       0.4483        0.7046  0.5478
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5000        0.9530       0.4138        0.7191  0.5642
+      2       0.4531        0.9635       0.4138        0.7196  0.5586
+      3       0.4375        1.0339       0.4483        0.7200  0.5706
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5469        0.6845       0.5517        0.7019  0.0886
+      2       0.5938        0.6805       0.5172        0.7011  0.0885
+      3       0.4844        0.7353       0.5172        0.7003  0.0885
+      4       0.5469        0.7266       0.5172        0.6996  0.0890
+      5       0.5312        0.7256       0.5172        0.6990  0.0915
+      6       0.4375        0.7298       0.5172        0.6985  0.0886
+      7       0.5000        0.7288       0.5172        0.6982  0.0885
+      8       0.4375        0.7391       0.5172        0.6978  0.0890
+      9       0.6250        0.6962       0.5172        0.6977  0.1010
+     10       0.5469        0.6834       0.4828        0.6975  0.0908
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5938        0.6919       0.7241        0.6898  0.0883
+      2       0.4062        0.7714       0.7241        0.6897  0.0882
+      3       0.5469        0.6963       0.7241        0.6897  0.0881
+      4       0.4375        0.7377       0.7241        0.6896  0.0880
+      5       0.5000        0.7363       0.6897        0.6896  0.0887
+      6       0.5156        0.7001       0.6897        0.6896  0.0885
+      7       0.5156        0.7378       0.6897        0.6895  0.0882
+      8       0.4531        0.7472       0.6552        0.6895  0.0884
+      9       0.4688        0.7408       0.6552        0.6894  0.0883
+     10       0.5938        0.6808       0.6552        0.6894  0.0963
+
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:35&lt;00:00, 18.18s/it]
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:35&lt;00:00, 17.95s/it]
+
+BNCI2014-004-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  120 events (all good), 3 – 7.5 s, baseline off, ~3.1 MB, data loaded,
+ &#39;left_hand&#39;: 60
+ &#39;right_hand&#39;: 60&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  120 events (all good), 3 – 7.5 s, baseline off, ~3.1 MB, data loaded,
+ &#39;left_hand&#39;: 60
+ &#39;right_hand&#39;: 60&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  160 events (all good), 3 – 7.5 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 80
+ &#39;right_hand&#39;: 80&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  160 events (all good), 3 – 7.5 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 80
+ &#39;right_hand&#39;: 80&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  160 events (all good), 3 – 7.5 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 80
+ &#39;right_hand&#39;: 80&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5000        0.9412       0.5333        0.7847  0.7594
+      2       0.5335        0.8727       0.5500        0.7416  0.7642
+      3       0.5737        0.8113       0.6167        0.7199  0.7664
+      4       0.5759        0.8205       0.6083        0.6926  0.7703
+      5       0.5781        0.7918       0.6167        0.6812  0.7616
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4911        0.9524       0.4500        0.8850  0.7658
+      2       0.5513        0.8934       0.5167        0.9415  0.7567
+      3       0.5536        0.7919       0.5083        0.8066  0.7593
+      4       0.6027        0.7941       0.5750        0.7251  0.7494
+      5       0.5781        0.8269       0.5917        0.6804  0.7643
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4375        1.0705       0.4821        0.9701  0.7582
+      2       0.4866        0.9521       0.5357        0.7768  0.7647
+      3       0.5022        0.9376       0.5714        0.7491  0.7575
+      4       0.5268        0.8963       0.5714        0.7131  0.7483
+      5       0.5067        0.9043       0.5714        0.6777  0.7632
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4688        0.9724       0.4554        1.0812  0.7603
+      2       0.4866        0.9542       0.4732        1.0143  0.7495
+      3       0.5312        0.8730       0.4554        0.8869  0.7498
+      4       0.5379        0.8111       0.5268        0.7505  0.7631
+      5       0.6071        0.7723       0.6161        0.6848  0.8119
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4866        0.9824       0.5357        0.8263  0.7640
+      2       0.5201        0.8853       0.5446        0.7731  0.7575
+      3       0.5446        0.8878       0.5357        0.7244  0.7493
+      4       0.6183        0.7720       0.5804        0.6973  0.7621
+      5       0.5625        0.7895       0.6071        0.6804  0.7561
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4888        0.9862       0.4167        0.7044  1.0668
+      2       0.4933        0.9458       0.5167        0.6997  1.0587
+      3       0.5223        0.9007       0.5333        0.6956  1.0566
+      4       0.5022        0.8895       0.5417        0.6919  1.0588
+      5       0.5067        0.8896       0.5417        0.6886  1.0523
+      6       0.4754        1.0003       0.5417        0.6855  1.0551
+      7       0.5000        0.9454       0.5750        0.6824  1.0513
+      8       0.5268        0.8909       0.5750        0.6796  1.0576
+      9       0.5089        0.8969       0.6000        0.6771  1.0559
+     10       0.4844        0.9135       0.6083        0.6750  1.0518
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5000        1.0360       0.4500        0.7294  1.0609
+      2       0.5000        0.9948       0.4083        0.7211  1.0557
+      3       0.4978        1.0685       0.3083        0.7159  1.0566
+      4       0.4710        1.0047       0.4250        0.7137  1.0481
+      5       0.4576        1.0780       0.5167        0.7134  1.0867
+      6       0.4509        1.0808       0.5083        0.7148  1.0493
+      7       0.4978        1.0492       0.5333        0.7170  1.0534
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4754        0.9803       0.5000        0.6897  1.0641
+      2       0.4621        1.0309       0.5089        0.6886  1.0644
+      3       0.5379        0.9047       0.5089        0.6874  1.0559
+      4       0.5179        0.9489       0.5268        0.6862  1.0493
+      5       0.4955        0.9089       0.5268        0.6851  1.0524
+      6       0.4777        0.9684       0.5536        0.6838  1.0460
+      7       0.4955        1.0081       0.5536        0.6823  1.0524
+      8       0.5179        0.9537       0.5536        0.6809  1.0828
+      9       0.4955        0.9676       0.5625        0.6793  1.0421
+     10       0.4621        1.0062       0.5625        0.6775  1.0502
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5379        0.8202       0.5446        0.6786  1.0581
+      2       0.5201        0.9291       0.5446        0.6790  1.0485
+      3       0.5022        0.8798       0.5446        0.6799  1.0450
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5112        0.9420       0.4107        0.7095  1.0523
+      2       0.4665        0.9857       0.4107        0.7093  1.0515
+      3       0.5647        0.8255       0.4375        0.7089  1.0493
+      4       0.4933        0.9507       0.4464        0.7084  1.0518
+      5       0.5089        0.9066       0.4464        0.7081  1.0445
+      6       0.5067        0.9766       0.4286        0.7079  1.0464
+      7       0.5246        0.9010       0.4107        0.7078  1.0450
+      8       0.5312        0.9108       0.4107        0.7076  1.0477
+      9       0.4688        0.9700       0.3929        0.7073  1.0593
+     10       0.4777        0.9619       0.4018        0.7067  1.0533
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4754        0.7894       0.4667        0.6935  0.2001
+      2       0.5022        0.7780       0.4750        0.6936  0.1877
+      3       0.4598        0.7743       0.5000        0.6937  0.1879
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4955        0.7396       0.4917        0.6933  0.1917
+      2       0.4464        0.7537       0.4833        0.6933  0.1921
+      3       0.5112        0.7361       0.4833        0.6933  0.1901
+      4       0.5156        0.7248       0.4667        0.6932  0.1878
+      5       0.4621        0.7457       0.4833        0.6931  0.1997
+      6       0.4888        0.7283       0.4917        0.6930  0.1905
+      7       0.4732        0.7365       0.5250        0.6928  0.1907
+      8       0.4531        0.7461       0.5167        0.6927  0.1901
+      9       0.4821        0.7176       0.5333        0.6925  0.1896
+     10       0.5156        0.7074       0.5417        0.6924  0.1956
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4978        0.7155       0.5179        0.6923  0.1899
+      2       0.4911        0.7209       0.5536        0.6921  0.1894
+      3       0.5223        0.7165       0.5893        0.6920  0.1893
+      4       0.5469        0.7056       0.5714        0.6919  0.1980
+      5       0.4464        0.7321       0.5446        0.6917  0.1894
+      6       0.5112        0.7238       0.5357        0.6916  0.1885
+      7       0.5067        0.7045       0.5714        0.6914  0.1894
+      8       0.5156        0.7187       0.5714        0.6912  0.1874
+      9       0.5491        0.7017       0.5804        0.6910  0.1991
+     10       0.5223        0.7005       0.5804        0.6909  0.1893
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4955        0.7409       0.4554        0.6935  0.1924
+      2       0.5045        0.7237       0.4375        0.6935  0.1967
+      3       0.5089        0.7291       0.4464        0.6935  0.1856
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4888        0.7749       0.4375        0.6947  0.2022
+      2       0.5045        0.7734       0.4732        0.6943  0.1885
+      3       0.4643        0.7679       0.4732        0.6940  0.1891
+      4       0.4710        0.7534       0.5000        0.6937  0.1865
+      5       0.4933        0.7502       0.5179        0.6934  0.1868
+      6       0.4866        0.7324       0.5268        0.6931  0.1864
+      7       0.5067        0.7374       0.5625        0.6928  0.1967
+      8       0.4911        0.7315       0.5446        0.6925  0.1851
+      9       0.5000        0.7234       0.5536        0.6922  0.1859
+     10       0.4866        0.7206       0.5536        0.6919  0.1861
+
+BNCI2014-004-CrossSession:  50%|#####     | 1/2 [01:17&lt;01:17, 77.33s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  120 events (all good), 3 – 7.5 s, baseline off, ~3.1 MB, data loaded,
+ &#39;left_hand&#39;: 60
+ &#39;right_hand&#39;: 60&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  120 events (all good), 3 – 7.5 s, baseline off, ~3.1 MB, data loaded,
+ &#39;left_hand&#39;: 60
+ &#39;right_hand&#39;: 60&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  160 events (all good), 3 – 7.5 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 80
+ &#39;right_hand&#39;: 80&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  120 events (all good), 3 – 7.5 s, baseline off, ~3.1 MB, data loaded,
+ &#39;left_hand&#39;: 60
+ &#39;right_hand&#39;: 60&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  160 events (all good), 3 – 7.5 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 80
+ &#39;right_hand&#39;: 80&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4576        0.9212       0.4375        1.1639  0.7525
+      2       0.5022        0.8720       0.4821        0.7823  0.7507
+      3       0.5335        0.8371       0.5089        0.7264  0.7602
+      4       0.4911        0.8292       0.5714        0.7198  0.7742
+      5       0.5268        0.8260       0.5536        0.7208  0.7589
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4955        0.9150       0.3929        1.2350  0.7655
+      2       0.4754        0.9059       0.4643        0.7855  0.7651
+      3       0.5446        0.8017       0.5536        0.7591  0.7626
+      4       0.5268        0.7990       0.5714        0.7518  0.7547
+      5       0.5379        0.7903       0.5446        0.7513  0.7653
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5078        0.9001       0.4904        1.2544  0.6607
+      2       0.5130        0.8694       0.5000        0.8166  0.6558
+      3       0.5417        0.8114       0.5288        0.7452  0.6476
+      4       0.5469        0.7868       0.5481        0.7339  0.6540
+      5       0.5651        0.7777       0.5385        0.7282  0.6464
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4442        0.9124       0.5804        1.2664  0.7575
+      2       0.5067        0.8408       0.5893        0.9500  0.7614
+      3       0.4821        0.8736       0.5714        0.7983  0.7596
+      4       0.5201        0.8417       0.5714        0.7416  0.7571
+      5       0.4933        0.8451       0.5179        0.7253  0.7493
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5286        0.8927       0.4327        0.9024  0.6672
+      2       0.4922        0.9043       0.5000        0.9090  0.6508
+      3       0.5234        0.8309       0.4904        0.9290  0.6442
+      4       0.4922        0.8563       0.5096        0.8682  0.6592
+      5       0.5286        0.8604       0.5192        0.7945  0.6484
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5067        0.9233       0.4911        0.6927  1.0451
+      2       0.4866        0.9082       0.4911        0.6967  1.0446
+      3       0.4978        0.9327       0.4196        0.7006  1.0472
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5000        0.9779       0.4643        0.6946  1.0581
+      2       0.4799        1.0024       0.5000        0.6943  1.0564
+      3       0.4576        1.0652       0.5179        0.6937  1.0578
+      4       0.4799        1.0139       0.5357        0.6934  1.0541
+      5       0.5112        0.9484       0.5268        0.6934  1.0537
+      6       0.5067        0.9618       0.5357        0.6934  1.0519
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4844        0.9147       0.4519        0.6934  0.9077
+      2       0.4740        0.9491       0.4519        0.6932  0.9077
+      3       0.5260        0.8936       0.5096        0.6933  0.9069
+      4       0.4766        0.9165       0.4904        0.6936  0.9059
+Stopping since valid_loss has not improved in the last 3 epochs.
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4911        0.9356       0.4018        0.7045  1.0479
+      2       0.4844        1.0194       0.3750        0.7040  1.0444
+      3       0.5067        0.9080       0.3482        0.7033  1.0452
+      4       0.5312        0.8844       0.3839        0.7028  1.0493
+      5       0.5223        0.9085       0.4196        0.7021  1.0495
+      6       0.4933        0.8773       0.4554        0.7016  1.0488
+      7       0.5067        0.9077       0.4286        0.7010  1.0628
+      8       0.5089        0.8976       0.4643        0.7006  1.0610
+      9       0.4799        0.9441       0.4821        0.7003  1.0582
+     10       0.4821        0.9229       0.4732        0.6999  1.0490
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4792        0.9699       0.5096        0.6884  0.9182
+      2       0.5260        0.9294       0.5481        0.6878  0.9062
+      3       0.4922        0.9464       0.5673        0.6874  0.9093
+      4       0.5260        0.8568       0.5865        0.6870  0.9119
+      5       0.5078        0.9270       0.5577        0.6867  0.9131
+      6       0.4974        0.9287       0.5865        0.6864  0.9078
+      7       0.4714        0.9542       0.5769        0.6861  0.9123
+      8       0.5182        0.8983       0.5769        0.6857  0.9147
+      9       0.4948        0.9354       0.5962        0.6854  0.9079
+     10       0.4609        0.9492       0.5962        0.6852  0.8993
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5179        0.7209       0.5268        0.6928  0.1947
+      2       0.5290        0.7116       0.4464        0.6937  0.1931
+      3       0.4978        0.7059       0.4286        0.6947  0.2039
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4866        0.7080       0.5089        0.6927  0.1961
+      2       0.5737        0.7016       0.5804        0.6916  0.1932
+      3       0.5156        0.7296       0.6071        0.6906  0.2034
+      4       0.5290        0.7251       0.6071        0.6897  0.1939
+      5       0.5379        0.7288       0.6071        0.6888  0.1934
+      6       0.5290        0.7088       0.6071        0.6881  0.1927
+      7       0.5223        0.7038       0.6071        0.6874  0.1898
+      8       0.5134        0.7170       0.6071        0.6866  0.2013
+      9       0.5357        0.7024       0.6071        0.6860  0.1923
+     10       0.5312        0.6996       0.6071        0.6854  0.1918
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5312        0.7613       0.5000        0.6930  0.1761
+      2       0.4870        0.7672       0.5000        0.6929  0.1667
+      3       0.4948        0.7538       0.5096        0.6929  0.1676
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5045        0.7197       0.4375        0.6944  0.2031
+      2       0.4710        0.7331       0.4464        0.6943  0.1905
+      3       0.4821        0.7176       0.4286        0.6943  0.1905
+      4       0.4866        0.7136       0.4464        0.6942  0.1914
+      5       0.4621        0.7352       0.4286        0.6941  0.1916
+      6       0.5134        0.7145       0.4375        0.6940  0.2016
+      7       0.4888        0.7101       0.4375        0.6939  0.1917
+      8       0.5089        0.7191       0.4375        0.6938  0.1924
+      9       0.5022        0.7145       0.4375        0.6938  0.1923
+     10       0.5469        0.6903       0.4464        0.6938  0.1903
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5182        0.7293       0.4038        0.6941  0.1678
+      2       0.5208        0.6988       0.4135        0.6942  0.1643
+      3       0.4792        0.7340       0.4712        0.6943  0.1641
+Stopping since valid_loss has not improved in the last 3 epochs.
+
+BNCI2014-004-CrossSession: 100%|##########| 2/2 [02:23&lt;00:00, 70.78s/it]
+BNCI2014-004-CrossSession: 100%|##########| 2/2 [02:23&lt;00:00, 71.77s/it]
+        dataset    evaluation                       pipeline  avg score
+0  BNCI2014-001  CrossSession    braindecode_ShallowFBCSPNet   0.508102
+1  BNCI2014-001  CrossSession  braindecode_EEGNetv4_resample   0.508005
+2  BNCI2014-001  CrossSession       braindecode_EEGInception   0.485629
+3  BNCI2014-004  CrossSession    braindecode_ShallowFBCSPNet   0.551979
+4  BNCI2014-004  CrossSession  braindecode_EEGNetv4_resample   0.489484
+5  BNCI2014-004  CrossSession       braindecode_EEGInception   0.519427
+</pre></div>
+</div>
+<p>The deep learning architectures implemented in MOABB using Braindecode are:</p>
+<ul class="simple">
+<li><p>Shallow Convolutional Network <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a></p></li>
+<li><p>Deep Convolutional Network <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a></p></li>
+<li><p>EEGNetv4 <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a></p></li>
+<li><p>EEGInception <a class="footnote-reference brackets" href="#id7" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a></p></li>
+</ul>
+<p>Benchmark prints a summary of the results. Detailed results are saved in a
+pandas dataframe, and can be used to generate figures. The analysis &amp; figures
+are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_benchmark_braindecode_001.png" srcset="../_images/sphx_glr_plot_benchmark_braindecode_001.png" alt="Scores per dataset and algorithm" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/analysis/plotting.py:70: UserWarning: The palette list has more values (6) than needed (3), which may not be intended.
+  sea.stripplot(
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>)</span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+Glasstetter, M., Eggensperger, K., Tangermann, M., … &amp; Ball, T. (2017).
+<a class="reference external" href="https://doi.org/10.1002/hbm.23730">Deep learning with convolutional neural networks for EEG decoding and
+visualization</a>.
+Human brain mapping, 38(11), 5391-5420.</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,
+Hung, C. P., &amp; Lance, B. J. (2018). <a class="reference external" href="https://doi.org/10.1088/1741-2552/aace8c">EEGNet: a compact convolutional neural
+network for EEG-based brain-computer interfaces.</a>
+Journal of neural engineering, 15(5), 056013.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">3</a><span class="fn-bracket">]</span></span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V., Vaquerizo-Villar,
+F., &amp; Hornero, R. (2020). <a class="reference external" href="https://doi.org/10.1109/TNSRE.2020.3048106">EEG-inception: A novel deep convolutional neural network
+for assistive ERP-based brain-computer interfaces.</a>
+IEEE Transactions on Neural Systems and Rehabilitation Engineering</p>
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+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_DL.html">Benchmarking on MOABB with Tensorflow deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_braindecode.html">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Benchmarking with MOABB with Grid Search</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-benchmark-grid-search-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-with-moabb-with-grid-search">
+<span id="sphx-glr-auto-examples-plot-benchmark-grid-search-py"></span><h1>Benchmarking with MOABB with Grid Search<a class="headerlink" href="#benchmarking-with-moabb-with-grid-search" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use MOABB to benchmark a set of pipelines
+on all available datasets. In particular we run the Gridsearch to select the best hyperparameter of some pipelines
+and save the gridsearch.
+For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
+to easily scale to many datasets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Igor Carrara &lt;igor.carrara@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">,</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.analysis.plotting</span> <span class="kn">import</span> <a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>In this example, we will use only the dataset ‘Zhou 2016’.</p>
+<section id="running-the-benchmark">
+<h2>Running the benchmark<a class="headerlink" href="#running-the-benchmark" title="Permalink to this heading">#</a></h2>
+<p>The benchmark is run using the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> function. You need to specify the
+folder containing the pipelines to use, the kind of evaluation and the paradigm
+to use. By default, the benchmark will use all available datasets for all
+paradigms listed in the pipelines. You could restrict to specific evaluation and
+paradigm using the <code class="docutils literal notranslate"><span class="pre">evaluations</span></code> and <code class="docutils literal notranslate"><span class="pre">paradigms</span></code> arguments.</p>
+<p>To save computation time, the results are cached. If you want to re-run the
+benchmark, you can set the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<p>It is possible to indicate the folder to cache the results and the one to save
+the analysis &amp; figures. By default, the results are saved in the <code class="docutils literal notranslate"><span class="pre">results</span></code>
+folder, and the analysis &amp; figures are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># In the results folder we will save the gridsearch evaluation</span>
+<span class="c1"># When write the pipeline in ylm file we need to specify the parameter that we want to test, in format</span>
+<span class="c1"># pipeline-name__estimator-name_parameter. Note that pipeline and estimator names MUST</span>
+<span class="c1"># be in lower case (no capital letters allowed).</span>
+<span class="c1"># If the grid search is already implemented it will load the previous results</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">benchmark</span></a><span class="p">(</span>
+    <span class="n">pipelines</span><span class="o">=</span><span class="s2">&quot;./pipelines_grid/&quot;</span><span class="p">,</span>
+    <span class="n">evaluations</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;WithinSession&quot;</span><span class="p">],</span>
+    <span class="n">paradigms</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LeftRightImagery&quot;</span><span class="p">],</span>
+    <span class="n">include_datasets</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Zhou2016&quot;</span><span class="p">],</span>
+    <span class="n">results</span><span class="o">=</span><span class="s2">&quot;./results/&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">output</span><span class="o">=</span><span class="s2">&quot;./benchmark/&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Zhou2016-WithinSession:   0%|          | 0/4 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~8.0 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~7.9 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+
+Zhou2016-WithinSession:  25%|##5       | 1/4 [00:25&lt;01:16, 25.40s/it]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~5.4 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+
+Zhou2016-WithinSession:  50%|#####     | 2/4 [00:48&lt;00:47, 23.79s/it]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+
+Zhou2016-WithinSession:  75%|#######5  | 3/4 [01:10&lt;00:23, 23.30s/it]/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning:   Could not parse meas date from the header. Setting to None.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/Zhou2016.py:111: RuntimeWarning: Could not define the number of bytes automatically. Defaulting to 2.
+  raw = read_raw_cnt(fname, preload=True, eog=[&quot;VEOU&quot;, &quot;VEOL&quot;])
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~5.4 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+
+Zhou2016-WithinSession: 100%|##########| 4/4 [01:33&lt;00:00, 22.94s/it]
+Zhou2016-WithinSession: 100%|##########| 4/4 [01:33&lt;00:00, 23.29s/it]
+    dataset     evaluation   pipeline  avg score
+0  Zhou2016  WithinSession    EN Grid   0.943531
+1  Zhou2016  WithinSession  CSP + LDA   0.937954
+2  Zhou2016  WithinSession         EN   0.943619
+</pre></div>
+</div>
+<p>Benchmark prints a summary of the results. Detailed results are saved in a
+pandas dataframe, and can be used to generate figures. The analysis &amp; figures
+are saved in the <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot" title="moabb.analysis.plotting.score_plot" class="sphx-glr-backref-module-moabb-analysis-plotting sphx-glr-backref-type-py-function"><span class="n">score_plot</span></a><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_benchmark_grid_search_001.png" srcset="../_images/sphx_glr_plot_benchmark_grid_search_001.png" alt="Scores per dataset and algorithm" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/analysis/plotting.py:70: UserWarning: The palette list has more values (6) than needed (3), which may not be intended.
+  sea.stripplot(
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  34.763 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  243 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-benchmark-grid-search-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/b129d24bc810ae3a961d5bdcb7413828/plot_benchmark_grid_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_benchmark_grid_search.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/47ae966f3ab36fc8884cda73c38e648d/plot_benchmark_grid_search.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_benchmark_grid_search.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Cross-session motor imagery with deep learning EEGNet v4 model</li>
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+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-braindecode-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cross-session-motor-imagery-with-deep-learning-eegnet-v4-model">
+<span id="sphx-glr-auto-examples-plot-braindecode-py"></span><h1>Cross-session motor imagery with deep learning EEGNet v4 model<a class="headerlink" href="#cross-session-motor-imagery-with-deep-learning-eegnet-v4-model" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to use BrainDecode in combination with MOABB evaluation.
+In this example, we use the architecture EEGNetv4.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Igor Carrara &lt;igor.carrara@inria.fr&gt;</span>
+<span class="c1">#          Bruno Aristimunha &lt;b.aristimunha@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">EEGNetv4</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EarlyStopping</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a>
+<span class="kn">from</span> <span class="nn">moabb.utils</span> <span class="kn">import</span> <a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a>
+
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Print Information PyTorch</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Torch Version: </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="c1"># Set up GPU if it is there</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GPU is&quot;</span><span class="p">,</span> <span class="s2">&quot;AVAILABLE&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;NOT AVAILABLE&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Torch Version: 1.13.1+cu117
+GPU is NOT AVAILABLE
+</pre></div>
+</div>
+<p>In this example, we will use only the dataset <code class="docutils literal notranslate"><span class="pre">BNCI2014_001</span></code>.</p>
+<section id="running-the-benchmark">
+<h2>Running the benchmark<a class="headerlink" href="#running-the-benchmark" title="Permalink to this heading">#</a></h2>
+<p>This example uses the CrossSession evaluation procedure. We focus on the dataset BNCI2014_001 and only on 1 subject
+to reduce computational time.</p>
+<p>To keep the computational time low, the epoch is reduced. In a real situation, we suggest using the following:
+EPOCH = 1000
+PATIENCE = 300</p>
+<p>This code is implemented to run on the CPU. If you’re using a GPU, do not use multithreading
+(i.e. set n_jobs=1)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set random seed to be able to reproduce results</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">42</span>
+<a href="../generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">setup_seed</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span>
+
+<span class="c1"># Ensure that all operations are deterministic on GPU (if used) for reproducibility</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">deterministic</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="c1"># Hyperparameter</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LEARNING_RATE</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>  <span class="c1"># parameter taken from Braindecode</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">WEIGHT_DECAY</span></a> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># parameter taken from Braindecode</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BATCH_SIZE</span></a> <span class="o">=</span> <span class="mi">64</span>  <span class="c1"># parameter taken from BrainDecode</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EPOCH</span></a> <span class="o">=</span> <span class="mi">10</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PATIENCE</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">tmax</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="c1"># Load the dataset</span>
+<a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;right_hand&quot;</span><span class="p">,</span> <span class="s2">&quot;left_hand&quot;</span><span class="p">]</span>
+<a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="n">tmax</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+</pre></div>
+</div>
+</section>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>In order to create a pipeline, we need to load a model from braindecode.
+the second step is to define a skorch model using EEGClassifier from braindecode
+that allows converting the PyTorch model in a scikit-learn classifier.
+Here, we will use the EEGNet v4 model <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> .
+This model has mandatory hyperparameters (the number of channels, the number of classes,
+and the temporal length of the input) but we do not need to specify them because they will
+be set dynamically by EEGClassifier using the input data during the call to the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> method.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define a Skorch classifier</span>
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <span class="n">module</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">EEGNetv4</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LEARNING_RATE</span></a><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BATCH_SIZE</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">EPOCH</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">),</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EarlyStopping</span></a><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="s2">&quot;valid_loss&quot;</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PATIENCE</span></a><span class="p">),</span>
+        <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+            <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;train_acc&quot;</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">),</span>
+        <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+            <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;valid_acc&quot;</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">),</span>
+    <span class="p">],</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>  <span class="c1"># Not printing the results for each epoch</span>
+<span class="p">)</span>
+
+<span class="c1"># Create the pipelines</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipes</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipes</span></a><span class="p">[</span><span class="s2">&quot;EEGNetV4&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">clf</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+
+<a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <span class="n">datasets</span><span class="o">=</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;braindecode_example&quot;</span><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">return_epochs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipes</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/analysis/results.py:93: RuntimeWarning: Setting non-standard config type: &quot;MOABB_RESULTS&quot;
+  set_config(&quot;MOABB_RESULTS&quot;, osp.join(osp.expanduser(&quot;~&quot;), &quot;mne_data&quot;))
+
+BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5156        0.7659       0.5172        0.6932  0.2121
+      2       0.5781        0.7363       0.5172        0.6925  0.1820
+      3       0.4375        0.7591       0.5517        0.6919  0.1808
+      4       0.5938        0.6669       0.5172        0.6913  0.2040
+      5       0.5781        0.6621       0.5172        0.6907  0.1819
+      6       0.5312        0.7085       0.4828        0.6903  0.1808
+      7       0.5469        0.7184       0.5172        0.6900  0.1797
+      8       0.4531        0.7167       0.5172        0.6897  0.1813
+      9       0.5938        0.6647       0.5172        0.6894  0.1812
+     10       0.5156        0.6890       0.5172        0.6890  0.1984
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.5156        0.7365       0.4138        0.7091  0.1826
+      2       0.5781        0.6883       0.4483        0.7075  0.1808
+      3       0.6094        0.6824       0.4483        0.7060  0.1827
+      4       0.5312        0.7151       0.4138        0.7049  0.1975
+      5       0.4844        0.7221       0.4138        0.7040  0.1793
+      6       0.5469        0.7016       0.4483        0.7032  0.1806
+      7       0.5781        0.6593       0.4483        0.7025  0.1804
+      8       0.5469        0.6934       0.4828        0.7019  0.1805
+      9       0.5469        0.6745       0.4828        0.7014  0.1794
+     10       0.5469        0.6874       0.4828        0.7010  0.2066
+
+BNCI2014-001-CrossSession:  50%|#####     | 1/2 [00:06&lt;00:06,  6.61s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;right_hand&#39;: 12
+ &#39;left_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
+  X = mne.concatenate_epochs(X)
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4219        0.7449       0.6207        0.6832  0.1823
+      2       0.4688        0.7881       0.6207        0.6865  0.1799
+      3       0.5000        0.7344       0.6207        0.6874  0.1904
+Stopping since valid_loss has not improved in the last 3 epochs.
+  epoch    train_acc    train_loss    valid_acc    valid_loss     dur
+-------  -----------  ------------  -----------  ------------  ------
+      1       0.4844        0.7113       0.5517        0.6901  0.1811
+      2       0.4531        0.7591       0.5517        0.6905  0.1803
+      3       0.4531        0.7438       0.5517        0.6908  0.1804
+Stopping since valid_loss has not improved in the last 3 epochs.
+
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:10&lt;00:00,  5.17s/it]
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:10&lt;00:00,  5.39s/it]
+      score      time  samples  ... n_sessions       dataset  pipeline
+0  0.521026  2.118917    144.0  ...          2  BNCI2014-001  EEGNetV4
+1  0.494985  2.049863    144.0  ...          2  BNCI2014-001  EEGNetV4
+2  0.493441  0.926365    144.0  ...          2  BNCI2014-001  EEGNetV4
+3  0.544367  0.923720    144.0  ...          2  BNCI2014-001  EEGNetV4
+
+[4 rows x 9 columns]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_braindecode_001.png" srcset="../_images/sphx_glr_plot_braindecode_001.png" alt="plot braindecode" class = "sphx-glr-single-img"/></section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id2" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,
+Hung, C. P., &amp; Lance, B. J. (2018). <a class="reference external" href="https://doi.org/10.1088/1741-2552/aace8c">EEGNet: a compact convolutional neural
+network for EEG-based brain-computer interfaces.</a>
+Journal of neural engineering, 15(5), 056013.</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  14.669 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  354 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-braindecode-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_braindecode.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/11d7e3106c779d2d16f2ad6e45bde649/plot_braindecode.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_braindecode.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
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+                  
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+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Benchmarking with MOABB with Grid Search</p>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cross-session-motor-imagery">
+<span id="sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"></span><h1>Cross-Session Motor Imagery<a class="headerlink" href="#cross-session-motor-imagery" title="Permalink to this heading">#</a></h1>
+<p>This example show how to perform a cross session motor imagery analysis on the
+very popular dataset 2a from the BCI competition IV.</p>
+<p>We will compare two pipelines :</p>
+<ul class="simple">
+<li><p>CSP+LDA</p></li>
+<li><p>Riemannian Geometry+Logistic Regression</p></li>
+</ul>
+<p>We will use the LeftRightImagery paradigm. This will restrict the analysis
+to two classes (left hand versus right hand) and use AUC as metric.</p>
+<p>The cross session evaluation context will evaluate performance using a leave
+one session out cross-validation. For each session in the dataset, a model
+is trained on every other session and performance are evaluated on the current
+session.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Alexandre Barachant &lt;alexandre.barachant@gmail.com&gt;</span>
+<span class="c1">#          Sylvain Chevallier &lt;sylvain.chevallier@uvsq.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.</p>
+<p>The CSP implementation is based on the MNE implementation. We selected 8 CSP
+components, as usually done in the literature.</p>
+<p>The Riemannian geometry pipeline consists in covariance estimation, tangent
+space mapping and finally a logistic regression for the classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LR&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lbfgs&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
+The evaluation will return a DataFrame containing a single AUC score for
+each subject / session of the dataset, and for each pipeline.</p>
+<p>Results are saved into the database, so that if you add a new pipeline, it
+will not run again the evaluation unless a parameter has changed. Results can
+be overwritten if necessary.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<span class="c1"># Because this is being auto-generated we only use 2 subjects</span>
+<a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+<a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+
+BNCI2014-001-CrossSession:  50%|#####     | 1/2 [00:04&lt;00:04,  4.14s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_function_transformer.py:312: FutureWarning: The current default of copy=False will change to copy=True in 1.7. Set the value of copy explicitly to avoid this warning
+  return func(X, **(kw_args if kw_args else {}))
+
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:08&lt;00:00,  4.01s/it]
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:08&lt;00:00,  4.03s/it]
+      score      time  samples  ... n_sessions       dataset  pipeline
+0  0.950424  0.257004    144.0  ...          2  BNCI2014-001     RG+LR
+1  0.962963  0.260381    144.0  ...          2  BNCI2014-001     RG+LR
+2  0.574846  0.241112    144.0  ...          2  BNCI2014-001     RG+LR
+3  0.585648  0.241607    144.0  ...          2  BNCI2014-001     RG+LR
+4  0.931713  0.326427    144.0  ...          2  BNCI2014-001   CSP+LDA
+
+[5 rows x 9 columns]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results. We first make a pointplot with the average
+performance of each pipeline across session and subjects.
+The second plot is a paired scatter plot. Each point representing the score
+of a single session. An algorithm will outperform another is most of the
+points are in its quadrant.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">stripplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+    <span class="n">jitter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">paired</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span>
+    <span class="n">values</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;session&quot;</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">paired</span> <span class="o">=</span> <span class="n">paired</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">paired</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;RG+LR&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">fit_reg</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_cross_session_motor_imagery_001.png" srcset="../_images/sphx_glr_plot_cross_session_motor_imagery_001.png" alt="plot cross session motor imagery" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/examples/plot_cross_session_motor_imagery.py:103: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
+  sns.stripplot(
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  10.019 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  332 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-cross-session-motor-imagery-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/82e3e6cb009bb9a9c444c6cff254cea8/plot_cross_session_motor_imagery.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_cross_session_motor_imagery.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/f1e72fecd296ad91b914eb3562c881ee/plot_cross_session_motor_imagery.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_cross_session_motor_imagery.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipelines">Create Pipelines</a></li>
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new file mode 100644
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+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.DemonsP300.html">moabb.datasets.DemonsP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.EPFLP300.html">moabb.datasets.EPFLP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Huebner2017.html">moabb.datasets.Huebner2017</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Huebner2018.html">moabb.datasets.Huebner2018</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_ERP.html">moabb.datasets.Lee2019_ERP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Sosulski2019.html">moabb.datasets.Sosulski2019</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Kalunga2016.html">moabb.datasets.Kalunga2016</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Nakanishi2015.html">moabb.datasets.Nakanishi2015</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Wang2016.html">moabb.datasets.Wang2016</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM1.html">moabb.datasets.MAMEM1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM2.html">moabb.datasets.MAMEM2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM3.html">moabb.datasets.MAMEM3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_SSVEP.html">moabb.datasets.Lee2019_SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2015.html">moabb.datasets.Thielen2015</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2021.html">moabb.datasets.Thielen2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP100.html">moabb.datasets.CastillosBurstVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.BaseDataset.html">moabb.datasets.base.BaseDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.CacheConfig.html">moabb.datasets.base.CacheConfig</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeDataset.html">moabb.datasets.fake.FakeDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeVirtualRealityDataset.html">moabb.datasets.fake.FakeVirtualRealityDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_path.html">moabb.datasets.download.data_path</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.data_dl.html">moabb.datasets.download.data_dl</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_issue_request.html">moabb.datasets.download.fs_issue_request</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_list.html">moabb.datasets.download.fs_get_file_list</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_hash.html">moabb.datasets.download.fs_get_file_hash</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_id.html">moabb.datasets.download.fs_get_file_id</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_name.html">moabb.datasets.download.fs_get_file_name</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.dataset_search.html">moabb.datasets.utils.dataset_search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.find_intersecting_channels.html">moabb.datasets.utils.find_intersecting_channels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014a_Il.html">moabb.datasets.compound_dataset.BI2014a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Cross-Session on Multiple Datasets</li>
+  </ul>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cross-session-on-multiple-datasets">
+<span id="sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"></span><h1>Cross-Session on Multiple Datasets<a class="headerlink" href="#cross-session-on-multiple-datasets" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to perform a cross-session analysis on two MI datasets
+using a CSP+LDA pipeline</p>
+<p>The cross session evaluation context will evaluate performance using a leave
+one session out cross-validation. For each session in the dataset, a model
+is trained on every other session and performance are evaluated on the current
+session.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Sylvain Chevallier &lt;sylvain.chevallier@uvsq.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="loading-dataset">
+<h2>Loading Dataset<a class="headerlink" href="#loading-dataset" title="Permalink to this heading">#</a></h2>
+<p>Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 session each</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a><span class="p">(),</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()]</span>
+<span class="k">for</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a>
+</pre></div>
+</div>
+</section>
+<section id="choose-paradigm">
+<h2>Choose Paradigm<a class="headerlink" href="#choose-paradigm" title="Permalink to this heading">#</a></h2>
+<p>We select the paradigm MI, applying a bandpass filter (8-35 Hz) on
+the data and we will keep only left- and right-hand motor imagery</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">(</span><span class="n">fmin</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">35</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Use the Common Spatial Patterns with 8 components and a Linear Discriminant
+Analysis classifier.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a><span class="p">[</span><span class="s2">&quot;CSP+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+</pre></div>
+</div>
+</section>
+<section id="get-data-optional">
+<h2>Get Data (optional)<a class="headerlink" href="#get-data-optional" title="Permalink to this heading">#</a></h2>
+<p>To get access to the EEG signals downloaded from the dataset, you could
+use <cite>dataset.get_data(subjects=[subject_id])</cite> to obtain the EEG under
+an MNE format, stored in a dictionary of sessions and runs.
+Otherwise, <cite>paradigm.get_data(dataset=dataset, subjects=[subject_id])</cite>
+allows to obtain the EEG data in sklearn format, the labels and the meta
+information. The data are preprocessed according to the paradigm
+requirements.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># X_all, labels_all, meta_all = [], [], []</span>
+<span class="c1"># for d in datasets:</span>
+<span class="c1">#     # sessions = d.get_data(subjects=[2])</span>
+<span class="c1">#     X, labels, meta = paradigm.get_data(dataset=d, subjects=[2])</span>
+<span class="c1">#     X_all.append(X)</span>
+<span class="c1">#     labels_all.append(labels)</span>
+<span class="c1">#     meta_all.append(meta)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>The evaluation will return a DataFrame containing a single AUC score for
+each subject / session of the dataset, and for each pipeline.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+
+<a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Zhou2016-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~8.0 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~7.9 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+Zhou2016-CrossSession:  50%|#####     | 1/2 [00:03&lt;00:03,  3.81s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  40 events (all good), 0 – 5 s, baseline off, ~5.4 MB, data loaded,
+ &#39;left_hand&#39;: 20
+ &#39;right_hand&#39;: 20&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+Zhou2016-CrossSession: 100%|##########| 2/2 [00:06&lt;00:00,  3.29s/it]
+Zhou2016-CrossSession: 100%|##########| 2/2 [00:06&lt;00:00,  3.37s/it]
+
+BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession:  50%|#####     | 1/2 [00:02&lt;00:02,  2.97s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:05&lt;00:00,  2.99s/it]
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:05&lt;00:00,  2.99s/it]
+      score      time  samples subject  ... channels  n_sessions   dataset pipeline
+0  0.851412  0.375433    200.0       1  ...       14           3  Zhou2016  CSP+LDA
+1  0.935200  0.263264    219.0       1  ...       14           3  Zhou2016  CSP+LDA
+2  0.939200  0.212352    219.0       1  ...       14           3  Zhou2016  CSP+LDA
+3  0.908000  0.195292    190.0       2  ...       14           3  Zhou2016  CSP+LDA
+4  0.770370  0.174092    200.0       2  ...       14           3  Zhou2016  CSP+LDA
+
+[5 rows x 9 columns]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results, indicating the score for each session and subject</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;session&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;subject&quot;</span><span class="p">,</span>
+    <span class="n">col</span><span class="o">=</span><span class="s2">&quot;dataset&quot;</span><span class="p">,</span>
+    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_cross_session_multiple_datasets_001.png" srcset="../_images/sphx_glr_plot_cross_session_multiple_datasets_001.png" alt="dataset = Zhou2016, dataset = BNCI2014-001" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  15.440 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  316 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-cross-session-multiple-datasets-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/fa1ca889a5b6c6678d3af932651fcf7f/plot_cross_session_multiple_datasets.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_cross_session_multiple_datasets.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/45332a7ce05680b6eb2282c04cab7d5b/plot_cross_session_multiple_datasets.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_cross_session_multiple_datasets.ipynb</span></code></a></p>
+</div>
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+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-dataset">Loading Dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choose-paradigm">Choose Paradigm</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipelines">Create Pipelines</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#get-data-optional">Get Data (optional)</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
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+<div class="header-article-items header-article__inner">
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Cross-Subject SSVEP</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
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+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cross-subject-ssvep">
+<span id="sphx-glr-auto-examples-plot-cross-subject-ssvep-py"></span><h1>Cross-Subject SSVEP<a class="headerlink" href="#cross-subject-ssvep" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to perform a cross-subject analysis on an SSVEP dataset.
+We will compare four pipelines :</p>
+<ul class="simple">
+<li><p>Riemannian Geometry</p></li>
+<li><p>CCA</p></li>
+<li><p>TRCA</p></li>
+<li><p>MsetCCA</p></li>
+</ul>
+<p>We will use the SSVEP paradigm, which uses the AUC as metric.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Sylvain Chevallier &lt;sylvain.chevallier@uvsq.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Kalunga2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSubjectEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">SSVEP</span></a><span class="p">,</span> <a href="../generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP" title="moabb.paradigms.FilterBankSSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FilterBankSSVEP</span></a>
+<span class="kn">from</span> <span class="nn">moabb.pipelines</span> <span class="kn">import</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_CCA</span></a><span class="p">,</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA" title="moabb.pipelines.classification.SSVEP_TRCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_TRCA</span></a><span class="p">,</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal" title="moabb.pipelines.features.ExtendedSSVEPSignal" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class"><span class="n">ExtendedSSVEPSignal</span></a><span class="p">,</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA" title="moabb.pipelines.classification.SSVEP_MsetCCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_MsetCCA</span></a>
+
+
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="loading-dataset">
+<h2>Loading Dataset<a class="headerlink" href="#loading-dataset" title="Permalink to this heading">#</a></h2>
+<p>We will load the data from the first 2 subjects of the <code class="docutils literal notranslate"><span class="pre">SSVEP_Exo</span></code> dataset
+and compare two algorithms on this set. One of the algorithms could only
+process class associated with a stimulation frequency, we will thus drop
+the resting class. As the resting class is the last defined class, picking
+the first three classes (out of four) allows to focus only on the stimulation
+frequency.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subject</span></a> <span class="o">=</span> <span class="mi">2</span>
+<a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Kalunga2016</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_subject</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">interval</span></a>
+</pre></div>
+</div>
+</section>
+<section id="choose-paradigm">
+<h2>Choose Paradigm<a class="headerlink" href="#choose-paradigm" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigms (SSVEP, SSVEP TRCA, SSVEP MsetCCA, and FilterBankSSVEP) and
+use the dataset Kalunga2016. All 3 SSVEP paradigms applied a bandpass filter (10-42 Hz) on
+the data, which include all stimuli frequencies and their first harmonics,
+while the FilterBankSSVEP paradigm uses as many bandpass filters as
+there are stimulation frequencies (here 3). For each stimulation frequency
+the EEG is filtered with a 1 Hz-wide bandpass filter centered on the
+frequency. This results in <code class="docutils literal notranslate"><span class="pre">n_classes</span></code> copies of the signal, filtered for each
+class, as used in the filterbank motor imagery paradigms.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">SSVEP</span></a><span class="p">(</span><span class="n">fmin</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm_TRCA</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">SSVEP</span></a><span class="p">(</span><span class="n">fmin</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm_MSET_CCA</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">SSVEP</span></a><span class="p">(</span><span class="n">fmin</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<a href="../generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP" title="moabb.paradigms.FilterBankSSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm_fb</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP" title="moabb.paradigms.FilterBankSSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FilterBankSSVEP</span></a><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Classes are defined by the frequency of the stimulation, here we use
+the first two frequencies of the dataset, 13 and 17 Hz.
+The evaluation function uses a LabelEncoder, transforming them
+to 0 and 1</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.used_events" title="moabb.paradigms.ssvep.BaseSSVEP.used_events" class="sphx-glr-backref-module-moabb-paradigms-ssvep sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">used_events</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.
+The first pipeline uses Riemannian geometry, by building an extended
+covariance matrices from the signal filtered around the considered
+frequency and applying a logistic regression in the tangent plane.
+The second pipeline relies on the above defined CCA classifier.
+The third pipeline relies on the TRCA algorithm,
+and the fourth uses the MsetCCA algorithm. Both CCA based methods
+(i.e. CCA and MsetCCA) used 3 CCA components.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_fb</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_fb</span></a><span class="p">[</span><span class="s2">&quot;RG+LogReg&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal" title="moabb.pipelines.features.ExtendedSSVEPSignal" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class"><span class="n">ExtendedSSVEPSignal</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lbfgs&quot;</span><span class="p">,</span> <span class="n">multi_class</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;CCA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_CCA</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="p">,</span> <span class="n">n_harmonics</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_TRCA</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_TRCA</span></a><span class="p">[</span><span class="s2">&quot;TRCA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA" title="moabb.pipelines.classification.SSVEP_TRCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_TRCA</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="p">))</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_MSET_CCA</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_MSET_CCA</span></a><span class="p">[</span><span class="s2">&quot;MSET_CCA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA" title="moabb.pipelines.classification.SSVEP_MsetCCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_MsetCCA</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="p">))</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>The evaluation will return a DataFrame containing an accuracy score for
+each subject / session of the dataset, and for each pipeline.</p>
+<p>Results are saved into the database, so that if you add a new pipeline, it
+will not run again the evaluation unless a parameter has changed. Results can
+be overwritten if necessary.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+
+<a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSubjectEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>Filter bank processing, determine the filter automatically from the
+stimulation frequency values of events.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation_fb</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSubjectEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP" title="moabb.paradigms.FilterBankSSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm_fb</span></a><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results_fb</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation_fb</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_fb</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>TRCA processing also relies on filter bank that is automatically designed.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation_TRCA</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSubjectEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm_TRCA</span></a><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results_TRCA</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation_TRCA</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_TRCA</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>MsetCCA processing</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation_MSET_CCA</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSubjectEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm_MSET_CCA</span></a><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><a href="../generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results_MSET_CCA</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation_MSET_CCA</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines_MSET_CCA</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>After processing the four, we simply concatenate the results.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">results</span><span class="p">,</span> <span class="n">results_fb</span><span class="p">,</span> <span class="n">results_TRCA</span><span class="p">,</span> <span class="n">results_MSET_CCA</span><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we display the results as stripplot, with a pointplot for error bar.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">stripplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span>
+    <span class="n">jitter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Accuracy&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_cross_subject_ssvep_001.png" srcset="../_images/sphx_glr_plot_cross_subject_ssvep_001.png" alt="plot cross subject ssvep" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  4.207 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  9 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-cross-subject-ssvep-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/5459b5c2be76a529eb282ae496a50415/plot_cross_subject_ssvep.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_cross_subject_ssvep.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/38abfe5b8b42dcb9de08824fd90d346b/plot_cross_subject_ssvep.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_cross_subject_ssvep.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
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+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Cache on disk intermediate data processing states</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-disk-cache-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cache-on-disk-intermediate-data-processing-states">
+<span id="sphx-glr-auto-examples-plot-disk-cache-py"></span><h1>Cache on disk intermediate data processing states<a class="headerlink" href="#cache-on-disk-intermediate-data-processing-states" title="Permalink to this heading">#</a></h1>
+<p>This example shows how intermediate data processing
+states can be cached on disk to speed up the loading
+of this data in subsequent calls.</p>
+<p>When a MOABB paradigm processes a dataset, it will
+first apply processing steps to the raw data, this is
+called the <code class="docutils literal notranslate"><span class="pre">raw_pipeline</span></code>. Then, it will convert the
+raw data into epochs and apply processing steps on the
+epochs, this is called the <code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code>.
+Finally, it will eventually convert the epochs into arrays,
+this is called the <code class="docutils literal notranslate"><span class="pre">array_pipeline</span></code>. In summary:</p>
+<p><code class="docutils literal notranslate"><span class="pre">raw_pipeline</span></code> –&gt; <code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code> –&gt; <code class="docutils literal notranslate"><span class="pre">array_pipeline</span></code></p>
+<p>After each step, MOABB offers the possibility to save on disk
+the result of the step. This is done by setting the <code class="docutils literal notranslate"><span class="pre">cache_config</span></code>
+parameter of the paradigm’s <code class="docutils literal notranslate"><span class="pre">get_data</span></code> method.
+The <code class="docutils literal notranslate"><span class="pre">cache_config</span></code> parameter is a dictionary that can take all
+the parameters of <code class="docutils literal notranslate"><span class="pre">moabb.datasets.base.CacheConfig</span></code> as keys,
+they are the following: <code class="docutils literal notranslate"><span class="pre">use</span></code>, <code class="docutils literal notranslate"><span class="pre">save_raw</span></code>, <code class="docutils literal notranslate"><span class="pre">save_epochs</span></code>,
+<code class="docutils literal notranslate"><span class="pre">save_array</span></code>, <code class="docutils literal notranslate"><span class="pre">overwrite_raw</span></code>, <code class="docutils literal notranslate"><span class="pre">overwrite_epochs</span></code>,
+<code class="docutils literal notranslate"><span class="pre">overwrite_array</span></code>, and <code class="docutils literal notranslate"><span class="pre">path</span></code>.  You can also directly pass a
+<code class="docutils literal notranslate"><span class="pre">CacheConfig</span></code> object as <code class="docutils literal notranslate"><span class="pre">cache_config</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">use=False</span></code>, the <code class="docutils literal notranslate"><span class="pre">save_*</span></code> and <code class="docutils literal notranslate"><span class="pre">overwrite_*</span></code>
+parameters are ignored.</p>
+<p>When trying to use the cache (i.e. <code class="docutils literal notranslate"><span class="pre">use=True</span></code>), MOABB will
+first check if there exist a cache of the result of the full
+pipeline (i.e. <code class="docutils literal notranslate"><span class="pre">raw_pipeline</span></code> –&gt; <code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code> -&gt;
+<code class="docutils literal notranslate"><span class="pre">array_pipeline</span></code>).
+If there is none, we remove the last step of the pipeline and
+look for its cached result. We keep removing steps and looking
+for a cached result until we find one or until we reach an
+empty pipeline.
+Every time, if the <code class="docutils literal notranslate"><span class="pre">overwrite_*</span></code> parameter
+of the corresponding step is true, we first try to erase the
+cache of this step.
+Once a cache has been found or the empty pipeline has been reached,
+depending on the case we either load the cache or the original dataset.
+Then, apply the missing steps one by one and save their result
+if their corresponding <code class="docutils literal notranslate"><span class="pre">save_*</span></code> parameter is true.</p>
+<p>By default, only the result of the <code class="docutils literal notranslate"><span class="pre">raw_pipeline</span></code> is saved.
+This is usually a good compromise between speed and disk space
+because, when using cached raw data, the epochs can be obtained
+without preloading the whole raw signals, only the necessary
+intervals. Yet, because only the raw data is cached, the epoching
+parameters can be changed without creating a new cache each time.
+However, if your epoching parameters are fixed, you can directly
+cache the epochs or the arrays to speed up the loading and
+reduce the disk space used.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">cache_config</span></code> parameter is also available for the <code class="docutils literal notranslate"><span class="pre">get_data</span></code>
+method of the datasets. It works the same way as for a
+paradigm except that it will save un-processed raw recordings.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pierre Guetschel &lt;pierre.guetschel@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">shutil</span>
+<span class="kn">import</span> <span class="nn">tempfile</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class"><span class="n">Path</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="basic-usage">
+<h2>Basic usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading">#</a></h2>
+<p>The <code class="docutils literal notranslate"><span class="pre">cache_config</span></code> parameter is a dictionary that has the
+following default values:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">default_cache_config</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">save_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">use</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>You don not need to specify all the keys of <code class="docutils literal notranslate"><span class="pre">cache_config</span></code>, only the ones
+you want to change.</p>
+<p>By default, the cache is saved at the MNE data directory (i.e. when
+<code class="docutils literal notranslate"><span class="pre">path=None</span></code>).  The MNE data directory can be found with
+<code class="docutils literal notranslate"><span class="pre">mne.get_config('MNE_DATA')</span></code>. For this example, we will save it  in a
+temporary directory instead:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class"><span class="n">Path</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">())</span>
+</pre></div>
+</div>
+<p>We will use the Zhou2016 dataset and the LeftRightImagery paradigm in this
+example, but this works for any dataset and paradigm pair.:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a><span class="p">()</span>
+<a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>And we will only use the first subject for this example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>Then, saving a cache can simply be done by setting the desired parameters
+in the <code class="docutils literal notranslate"><span class="pre">cache_config</span></code> dictionary:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">use</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">save_raw</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">save_epochs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">save_array</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~8.0 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~7.9 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+</pre></div>
+</div>
+</section>
+<section id="time-comparison">
+<h2>Time comparison<a class="headerlink" href="#time-comparison" title="Permalink to this heading">#</a></h2>
+<p>Now, we will compare the time it takes to load the with different levels of
+cache. For this, we will use the cache saved in the previous block and
+overwrite the steps results one by one so that we can compare the time it
+takes to load the data and compute the missing steps with an increasing
+number of missing steps.</p>
+<p>Using array cache:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">use</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">,</span>
+    <span class="n">save_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_array</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a>
+</pre></div>
+</div>
+<p>Using epochs cache:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">use</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">,</span>
+    <span class="n">save_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_array</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_epochs</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a>
+</pre></div>
+</div>
+<p>Using raw cache:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">use</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">,</span>
+    <span class="n">save_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_epochs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">overwrite_array</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_raw</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~8.0 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~7.9 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+</pre></div>
+</div>
+<p>Using no cache:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">use</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">,</span>
+    <span class="n">save_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_raw</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">overwrite_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cache_config</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_nocache</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t0</span></a>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  60 events (all good), 0 – 5 s, baseline off, ~8.0 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 30&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  59 events (all good), 0 – 5 s, baseline off, ~7.9 MB, data loaded,
+ &#39;left_hand&#39;: 30
+ &#39;right_hand&#39;: 29&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  50 events (all good), 0 – 5 s, baseline off, ~6.7 MB, data loaded,
+ &#39;left_hand&#39;: 25
+ &#39;right_hand&#39;: 25&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+</pre></div>
+</div>
+<p>Time needed to load the data with different levels of cache:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using array cache: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_array</span></a><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using epochs cache: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_epochs</span></a><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using raw cache: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_raw</span></a><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Without cache: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t_nocache</span></a><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using array cache: 0.31 seconds
+Using epochs cache: 0.49 seconds
+Using raw cache: 0.78 seconds
+Without cache: 2.35 seconds
+</pre></div>
+</div>
+<p>As you can see, using a raw cache is more than 5 times faster than
+without cache.
+This is because when using the raw cache, the data is not preloaded, only
+the desired epochs are loaded in memory.</p>
+<p>Using the epochs cache is a little faster than the raw cache. This is because
+there are several preprocessing steps done after the epoching by the
+<code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code>. This difference would be greater if the <code class="docutils literal notranslate"><span class="pre">resample</span></code>
+argument was different that the sampling frequency of the dataset. Indeed,
+the data loading time is directly proportional to its sampling frequency
+and the resampling is done by the <code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code>.</p>
+<p>Finally, we observe very little difference between array and epochs cache.
+The main interest of the array cache is when the user passes a
+computationally heavy but fixed additional preprocessing (for example
+computing the covariance matrices of the epochs). This can be done by using
+the <code class="docutils literal notranslate"><span class="pre">postprocess_pipeline</span></code> argument. The output of this additional pipeline
+(necessary a numpy array) will be saved to avoid re-computing it each time.</p>
+</section>
+<section id="technical-details">
+<h2>Technical details<a class="headerlink" href="#technical-details" title="Permalink to this heading">#</a></h2>
+<p>Under the hood, the cache is saved on disk in a Brain Imaging Data Structure
+(BIDS) compliant format. More details on this structure can be found in the
+tutorial <span class="xref std std-doc">./plot_bids_conversion</span>.</p>
+<p>However, there are two particular aspects of the way MOABB saves the data
+that are not specific to BIDS:</p>
+<ul class="simple">
+<li><p>For each file, we set a
+<a class="reference external" href="https://bids-specification.readthedocs.io/en/stable/appendices/entities.html#desc">description key</a>.
+This key is a code that corresponds to a hash of the
+pipeline that was used to generate the data (i.e. from raw to the state
+of the cache). This code is unique for each different pipeline and allows
+to identify all the files that were generated by the same pipeline.</p></li>
+<li><p>Once we finish saving all the files for a given combination of dataset,
+subject, and pipeline, we write a file ending in <code class="docutils literal notranslate"><span class="pre">&quot;_lockfile.json&quot;</span></code> at
+the root directory of this subject. This file serves two purposes:</p>
+<ul>
+<li><p>It indicates that the cache is complete for this subject and pipeline.
+If it is not present, it means that something went wrong during the
+saving process and the cache is incomplete.</p></li>
+<li><p>The file contains the un-hashed string representation of the pipeline.
+Therefore, it can be used to identify the pipeline used without having
+to decode the description key.</p></li>
+</ul>
+</li>
+</ul>
+</section>
+<section id="cleanup">
+<h2>Cleanup<a class="headerlink" href="#cleanup" title="Permalink to this heading">#</a></h2>
+<p>Finally, we can delete the temporary folder:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/shutil.html#shutil.rmtree" title="shutil.rmtree" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  27.185 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  325 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-disk-cache-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/de620aa43eea2fc28a9b3587adf0d1f8/plot_disk_cache.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_disk_cache.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/1cb0649d22103248d85dfccb151aabae/plot_disk_cache.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_disk_cache.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basic-usage">Basic usage</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#time-comparison">Time comparison</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#technical-details">Technical details</a></li>
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diff --git a/docs/auto_examples/plot_explore_paradigm.html b/docs/auto_examples/plot_explore_paradigm.html
new file mode 100644
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+++ b/docs/auto_examples/plot_explore_paradigm.html
@@ -0,0 +1,1449 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
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+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../_static/moabb_logo.png" type="image/png">
+    <title>Explore Paradigm Object &#8212; moabb 1.0.0-dev documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
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+    <li class="breadcrumb-item active" aria-current="page">Explore Paradigm Object</li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="explore-paradigm-object">
+<span id="sphx-glr-auto-examples-plot-explore-paradigm-py"></span><h1>Explore Paradigm Object<a class="headerlink" href="#explore-paradigm-object" title="Permalink to this heading">#</a></h1>
+<p>A paradigm defines how the raw data will be converted to trials ready
+to be processed by a decoding algorithm. This is a function of the paradigm
+used, i.e. in motor imagery one can have two-class, multi-class,
+or continuous paradigms; similarly, different preprocessing is necessary
+for ERP vs ERD paradigms.</p>
+<p>A paradigm also defines the appropriate evaluation metric, for example AUC
+for binary classification problems, accuracy for multiclass, or kappa
+coefficients for continuous paradigms.</p>
+<dl class="simple">
+<dt>This tutorial explores the paradigm object, with 3 examples of paradigm :</dt><dd><ul class="simple">
+<li><p>MotorImagery</p></li>
+<li><p>FilterBankMotorImagery</p></li>
+<li><p>LeftRightImagery</p></li>
+</ul>
+</dd>
+</dl>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Alexandre Barachant &lt;alexandre.barachant@gmail.com&gt;</span>
+<span class="c1">#          Sylvain Chevallier &lt;sylvain.chevallier@uvsq.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery" title="moabb.paradigms.FilterBankMotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FilterBankMotorImagery</span></a><span class="p">,</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">,</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a>
+
+
+<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="motorimagery">
+<h2>MotorImagery<a class="headerlink" href="#motorimagery" title="Permalink to this heading">#</a></h2>
+<p>First, let’s take an example of the MotorImagery paradigm.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>N-class motor imagery.
+
+    Metric is &#39;roc-auc&#39; if 2 classes and &#39;accuracy&#39; if more
+
+    Parameters
+    -----------
+
+    events: List of str
+        event labels used to filter datasets (e.g. if only motor imagery is
+        desired).
+
+    n_classes: int,
+        number of classes each dataset must have. If events is given,
+        requires all imagery sorts to be within the events list.
+
+    fmin: float (default 8)
+        cutoff frequency (Hz) for the high pass filter
+
+    fmax: float (default 32)
+        cutoff frequency (Hz) for the low pass filter
+
+    tmin: float (default 0.0)
+        Start time (in second) of the epoch, relative to the dataset specific
+        task interval e.g. tmin = 1 would mean the epoch will start 1 second
+        after the beginning of the task as defined by the dataset.
+
+    tmax: float | None, (default None)
+        End time (in second) of the epoch, relative to the beginning of the
+        dataset specific task interval. tmax = 5 would mean the epoch will end
+        5 second after the beginning of the task as defined in the dataset. If
+        None, use the dataset value.
+
+    baseline: None | tuple of length 2
+            The time interval to consider as “baseline” when applying baseline
+            correction. If None, do not apply baseline correction.
+            If a tuple (a, b), the interval is between a and b (in seconds),
+            including the endpoints.
+            Correction is applied by computing the mean of the baseline period
+            and subtracting it from the data (see mne.Epochs)
+
+    channels: list of str | None (default None)
+        list of channel to select. If None, use all EEG channels available in
+        the dataset.
+
+    resample: float | None (default None)
+        If not None, resample the eeg data with the sampling rate provided.
+</pre></div>
+</div>
+<p>The function <cite>get_data</cite> allow you to access preprocessed data from a dataset.
+this function will return 3 objects. A numpy array containing the
+preprocessed EEG data, the labels, and a dataframe with metadata.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Return the data for a list of subject.
+
+return the data, labels and a dataframe with metadata. the dataframe
+will contain at least the following columns
+
+- subject : the subject indice
+- session : the session indice
+- run : the run indice
+
+Parameters
+----------
+dataset:
+    A dataset instance.
+subjects: List of int
+    List of subject number
+return_epochs: boolean
+    This flag specifies whether to return only the data array or the
+    complete processed mne.Epochs
+return_raws: boolean
+    To return raw files and events, to ensure compatibility with braindecode.
+    Mutually exclusive with return_epochs
+cache_config: dict | CacheConfig
+    Configuration for caching of datasets. See :class:`moabb.datasets.base.CacheConfig` for details.
+postprocess_pipeline: Pipeline | None
+    Optional pipeline to apply to the data after the preprocessing.
+    This pipeline will either receive :class:`mne.io.BaseRaw`, :class:`mne.Epochs`
+    or :func:`np.ndarray` as input, depending on the values of ``return_epochs``
+    and ``return_raws``.
+    This pipeline must return an ``np.ndarray``.
+    This pipeline must be &quot;fixed&quot; because it will not be trained,
+    i.e. no call to ``fit`` will be made.
+
+Returns
+-------
+X : Union[np.ndarray, mne.Epochs]
+    the data that will be used as features for the model
+    Note: if return_epochs=True,  this is mne.Epochs
+    if return_epochs=False, this is np.ndarray
+labels: np.ndarray
+    the labels for training / evaluating the model
+metadata: pd.DataFrame
+    A dataframe containing the metadata.
+</pre></div>
+</div>
+<p>Lets take the example of the BNCI2014_001 dataset, known as the dataset IIa
+from the BCI competition IV. We will load the data from the subject 1.
+When calling <cite>get_data</cite>, the paradigm will retrieve the data from the
+specified list of subjects, apply preprocessing (by default, a bandpass
+between 7 and 35 Hz), epoch the data (with interval specified by the dataset,
+unless superseded by the paradigm) and return the corresponding objects.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <span class="n">metadata</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  48 events (all good), 2 – 6 s, baseline off, ~8.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12
+ &#39;feet&#39;: 12
+ &#39;tongue&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+</pre></div>
+</div>
+<p>The epoched data is a 3D array, with epochs on the first dimension (here
+576 trials), channels on the second (22 channels) and time sample on the last
+one.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(576, 22, 1001)
+</pre></div>
+</div>
+<p>Labels contains the labels corresponding to each trial. in the case of this
+dataset, we have the 4 types of motor imagery that was performed.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;feet&#39; &#39;left_hand&#39; &#39;right_hand&#39; &#39;tongue&#39;]
+</pre></div>
+</div>
+<p>Metadata have at least 3 columns: subject, session and run.</p>
+<ul class="simple">
+<li><p>subject is the subject id of the corresponding trial</p></li>
+<li><p>session is the session id. A session denotes a recording made without
+removing the EEG cap.</p></li>
+<li><p>run is the individual continuous recording made during a session. A session
+may or may not contain multiple runs.</p></li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">metadata</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>   subject session run
+0        1  0train   0
+1        1  0train   0
+2        1  0train   0
+3        1  0train   0
+4        1  0train   0
+</pre></div>
+</div>
+<p>For this data, we have one subject, 2 sessions (2 different recording days)
+and 6 runs per session.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">metadata</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>        subject session  run
+count     576.0     576  576
+unique      NaN       2    6
+top         NaN  0train    0
+freq        NaN     288   96
+mean        1.0     NaN  NaN
+std         0.0     NaN  NaN
+min         1.0     NaN  NaN
+25%         1.0     NaN  NaN
+50%         1.0     NaN  NaN
+75%         1.0     NaN  NaN
+max         1.0     NaN  NaN
+</pre></div>
+</div>
+<p>Paradigm objects can also return the list of all dataset compatible. Here
+it will return the list all the imagery datasets from the MOABB.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">compatible_datasets</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" title="moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" class="sphx-glr-backref-module-moabb-paradigms-motor_imagery sphx-glr-backref-type-py-property"><span class="n">paradigm</span><span class="o">.</span><span class="n">datasets</span></a>
+<span class="nb">print</span><span class="p">([</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">code</span></a> <span class="k">for</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">compatible_datasets</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;AlexandreMotorImagery&#39;, &#39;BNCI2014-001&#39;, &#39;BNCI2014-002&#39;, &#39;BNCI2014-004&#39;, &#39;BNCI2015-001&#39;, &#39;BNCI2015-004&#39;, &#39;Cho2017&#39;, &#39;FakeDataset-imagery-10-2--60-60--120-120--fake1-fake2-fake3--c3-cz-c4&#39;, &#39;GrosseWentrup2009&#39;, &#39;Lee2019-MI&#39;, &#39;Ofner2017&#39;, &#39;PhysionetMotorImagery&#39;, &#39;Schirrmeister2017&#39;, &#39;Shin2017A&#39;, &#39;Weibo2014&#39;, &#39;Zhou2016&#39;]
+</pre></div>
+</div>
+</section>
+<section id="filterbank-motorimagery">
+<h2>FilterBank MotorImagery<a class="headerlink" href="#filterbank-motorimagery" title="Permalink to this heading">#</a></h2>
+<p>FilterBankMotorImagery is the same paradigm, but with a different
+preprocessing. In this case, it applies a bank of 6 bandpass filter on the data
+before concatenating the output.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery" title="moabb.paradigms.FilterBankMotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FilterBankMotorImagery</span></a><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Filter bank n-class motor imagery.
+
+    Metric is &#39;roc-auc&#39; if 2 classes and &#39;accuracy&#39; if more
+
+    Parameters
+    -----------
+
+    events: List of str
+        event labels used to filter datasets (e.g. if only motor imagery is
+        desired).
+
+    n_classes: int,
+        number of classes each dataset must have. If events is given,
+        requires all imagery sorts to be within the events list.
+</pre></div>
+</div>
+<p>Therefore, the output X is a 4D array, with trial x channel x time x filter</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <span class="n">metadata</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+(288, 22, 1001, 6)
+</pre></div>
+</div>
+</section>
+<section id="leftright-motorimagery">
+<h2>LeftRight MotorImagery<a class="headerlink" href="#leftright-motorimagery" title="Permalink to this heading">#</a></h2>
+<p>LeftRightImagery is a variation over the BaseMotorImagery paradigm,
+restricted to left- and right-hand events.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Motor Imagery for left hand/right hand classification.
+
+    Metric is &#39;roc_auc&#39;
+</pre></div>
+</div>
+<p>The compatible dataset list is a subset of motor imagery dataset that
+contains at least left and right hand events.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">compatible_datasets</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" title="moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" class="sphx-glr-backref-module-moabb-paradigms-motor_imagery sphx-glr-backref-type-py-property"><span class="n">paradigm</span><span class="o">.</span><span class="n">datasets</span></a>
+<span class="nb">print</span><span class="p">([</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">code</span></a> <span class="k">for</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">compatible_datasets</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;BNCI2014-001&#39;, &#39;BNCI2014-004&#39;, &#39;Cho2017&#39;, &#39;GrosseWentrup2009&#39;, &#39;Lee2019-MI&#39;, &#39;PhysionetMotorImagery&#39;, &#39;Schirrmeister2017&#39;, &#39;Shin2017A&#39;, &#39;Weibo2014&#39;, &#39;Zhou2016&#39;]
+</pre></div>
+</div>
+<p>So if we apply this to our original dataset, it will only return trials
+corresponding to left- and right-hand motor imagination.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <span class="n">metadata</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+[&#39;left_hand&#39; &#39;right_hand&#39;]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  23.021 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  799 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-explore-paradigm-py">
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+<p><a class="reference download internal" download="" href="../_downloads/c0e8dffad0e66be84b9a762a9f24a516/plot_explore_paradigm.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_explore_paradigm.ipynb</span></code></a></p>
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new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
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+    <li class="breadcrumb-item active" aria-current="page">Fixed interval windows processing</li>
+  </ul>
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+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="fixed-interval-windows-processing">
+<span id="sphx-glr-auto-examples-plot-fixed-interval-windows-py"></span><h1>Fixed interval windows processing<a class="headerlink" href="#fixed-interval-windows-processing" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to process a dataset using the
+<a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.FixedIntervalWindowsProcessing</span></code></a> paradigm. This paradigm
+creates epochs at fixed intervals, ignoring the stim
+channel and events of the datasets. Therefore, it is
+compatible with all the datasets. Unfortunately,
+this paradigm is not compatible with the MOABB evaluation
+framework. However, it can be used to process datasets
+for unsupervised algorithms.</p>
+<p>In this example, we will use the Zhou2016 dataset because
+it is relatively small and can be downloaded quickly.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pierre Guetschel &lt;pierre.guetschel@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">moabb</span> <span class="kn">import</span> <a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FixedIntervalWindowsProcessing</span></a><span class="p">,</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="process-a-dataset">
+<h2>Process a dataset<a class="headerlink" href="#process-a-dataset" title="Permalink to this heading">#</a></h2>
+<p>To process a dataset with
+<a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.FixedIntervalWindowsProcessing</span></code></a> , you can use the
+method as with every other paradigm. The only additional parameters are
+<code class="docutils literal notranslate"><span class="pre">length</span></code>, <code class="docutils literal notranslate"><span class="pre">stride</span></code>, <code class="docutils literal notranslate"><span class="pre">start_offset</span></code>, and <code class="docutils literal notranslate"><span class="pre">stop_offset</span></code>. They are
+all parametrised in seconds. <code class="docutils literal notranslate"><span class="pre">length</span></code> is the length of the epochs,
+<code class="docutils literal notranslate"><span class="pre">stride</span></code> is the time between the onset of two consecutive epochs,
+<code class="docutils literal notranslate"><span class="pre">start_offset</span></code> is the offset between each run start and their first
+epoch, and <code class="docutils literal notranslate"><span class="pre">stop_offset</span></code> is the offset between each run start and their
+last epoch. The default values are <code class="docutils literal notranslate"><span class="pre">length=5</span></code>, <code class="docutils literal notranslate"><span class="pre">stride=10</span></code>,
+<code class="docutils literal notranslate"><span class="pre">start_offset=0</span></code>, and <code class="docutils literal notranslate"><span class="pre">stop_offset=None</span></code> (i.e. end of the run).</p>
+<p>An example usage of <a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.FixedIntervalWindowsProcessing</span></code></a>
+with the <a class="reference internal" href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.Zhou2016</span></code></a> dataset:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a><span class="p">()</span>
+<a href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">FixedIntervalWindowsProcessing</span></a><span class="p">(</span>
+    <span class="c1"># new parameters:</span>
+    <span class="n">length</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">stride</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="n">start_offset</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
+    <span class="n">stop_offset</span><span class="o">=</span><span class="mi">900</span><span class="p">,</span>  <span class="c1"># we epoch 10 minutes per run, starting at 5 minutes (i.e. 300 seconds)</span>
+    <span class="c1"># parameters common with other paradigms:</span>
+    <span class="n">resample</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">fmin</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+    <span class="n">fmax</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span>
+    <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">channels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">,</span> <span class="n">metadata</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">processing</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">subjects</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  10 events (all good), 0 – 100 s, baseline off, ~26.7 MB, data loaded,
+ &#39;Window&#39;: 10&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  10 events (all good), 0 – 100 s, baseline off, ~26.7 MB, data loaded,
+ &#39;Window&#39;: 10&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  10 events (all good), 0 – 100 s, baseline off, ~26.7 MB, data loaded,
+ &#39;Window&#39;: 10&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  10 events (all good), 0 – 100 s, baseline off, ~26.7 MB, data loaded,
+ &#39;Window&#39;: 10&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  10 events (all good), 0 – 100 s, baseline off, ~26.7 MB, data loaded,
+ &#39;Window&#39;: 10&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  10 events (all good), 0 – 100 s, baseline off, ~26.7 MB, data loaded,
+ &#39;Window&#39;: 10&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+</pre></div>
+</div>
+<p>In this dataset, there are three sessions per subject and two runs per
+session:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">column</span></a> <span class="ow">in</span> <span class="n">metadata</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">column</span></a><span class="si">}</span><span class="s2">s: </span><span class="si">{</span><span class="n">metadata</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">column</span></a><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>subjects: [1]
+sessions: [&#39;0&#39; &#39;1&#39; &#39;2&#39;]
+runs: [&#39;0&#39; &#39;1&#39;]
+</pre></div>
+</div>
+<p>We expect to obtained <code class="docutils literal notranslate"><span class="pre">(stop_offset</span> <span class="pre">-</span> <span class="pre">start_offset</span> <span class="pre">-</span> <span class="pre">length)</span> <span class="pre">/</span> <span class="pre">stride</span></code>;
+i.e. <span class="math">(900-300-100)/50=10</span> epochs per run. Here we have 3*2=6 runs.
+And indeed, we obtain
+a total of <span class="math">6*10=60</span> epochs:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of epochs: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Number of epochs: 60
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To apply a bank of bandpass filters, you can use the
+<a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html#moabb.paradigms.FilterBankFixedIntervalWindowsProcessing" title="moabb.paradigms.FilterBankFixedIntervalWindowsProcessing"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</span></code></a>
+paradigm instead.</p>
+</div>
+</section>
+<section id="print-the-events">
+<h2>Print the events<a class="headerlink" href="#print-the-events" title="Permalink to this heading">#</a></h2>
+<p>We can print the position of the created epochs within the run next to
+the original events of the dataset. For this, we will first instantiate
+a <a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.MotorImagery</span></code></a> paradigm to recover the original
+events of the dataset:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">MotorImagery</span></a><span class="p">(</span>
+    <span class="n">resample</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">fmin</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+    <span class="n">fmax</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span>
+    <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">channels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>Then, we can recover the events of both paradigms using the
+<code class="docutils literal notranslate"><span class="pre">_get_events_pipeline</span></code> method:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">events_pipeline_dataset</span> <span class="o">=</span> <a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">.</span><span class="n">_get_events_pipeline</span><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+<span class="n">events_pipeline_fixed</span> <span class="o">=</span> <a href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing</span></a><span class="o">.</span><span class="n">_get_events_pipeline</span><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+<span class="n">raw</span> <span class="o">=</span> <a href="../generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset.get_data" title="moabb.datasets.base.BaseDataset.get_data" class="sphx-glr-backref-module-moabb-datasets-base sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><span class="n">subjects</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])[</span><span class="mi">1</span><span class="p">][</span><span class="s2">&quot;0&quot;</span><span class="p">][</span><span class="s2">&quot;0&quot;</span><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events_dataset</span></a> <span class="o">=</span> <span class="n">events_pipeline_dataset</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">raw</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events_fixed</span></a> <span class="o">=</span> <span class="n">events_pipeline_fixed</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">raw</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">([</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events_dataset</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events_fixed</span></a><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_id</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="o">**</span><a href="../generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">.</span><span class="n">used_events</span><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">),</span> <span class="o">**</span><a href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">processing</span></a><span class="o">.</span><span class="n">used_events</span><span class="p">(</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<p>Finally, we can plot the events. The artificial events created by
+<a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.FixedIntervalWindowsProcessing</span></code></a> are named
+<code class="docutils literal notranslate"><span class="pre">&quot;Windows&quot;</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.viz.plot_events.html#mne.viz.plot_events" title="mne.viz.plot_events" class="sphx-glr-backref-module-mne-viz sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">viz</span><span class="o">.</span><span class="n">plot_events</span></a><span class="p">(</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">events</span></a><span class="p">,</span>
+    <span class="n">sfreq</span><span class="o">=</span><a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne._fiff.meas_info.Info" class="sphx-glr-backref-module-mne-_fiff-meas_info sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw</span><span class="o">.</span><span class="n">info</span></a><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_id</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_id</span></a><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.subplots_adjust" title="matplotlib.figure.Figure.subplots_adjust" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_fixed_interval_windows_001.png" srcset="../_images/sphx_glr_plot_fixed_interval_windows_001.png" alt="plot fixed interval windows" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/moabb/moabb/examples/plot_fixed_interval_windows.py:122: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.
+  fig.subplots_adjust(right=0.7)
+</pre></div>
+</div>
+<p>We can see that the epochs were effectively created at a fixed interval
+every 50 seconds between 300 and 900 seconds, and ignoring
+the original events of the dataset.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.223 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  304 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-fixed-interval-windows-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/fb557f4e0ac4b423b8911acc0958ae76/plot_fixed_interval_windows.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_fixed_interval_windows.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/2a0ca02a0f1b91ece5afbc9bdeceffe4/plot_fixed_interval_windows.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_fixed_interval_windows.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
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+                <footer class="bd-footer-article">
+                  
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+        <p class="prev-next-title">Explore Paradigm Object</p>
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+    <i class="fa-solid fa-list"></i> On this page
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#process-a-dataset">Process a dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#print-the-events">Print the events</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_SSVEP.html">moabb.datasets.Lee2019_SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2015.html">moabb.datasets.Thielen2015</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2021.html">moabb.datasets.Thielen2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP100.html">moabb.datasets.CastillosBurstVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.BaseDataset.html">moabb.datasets.base.BaseDataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Within Session P300</li>
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+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-within-session-p300-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="within-session-p300">
+<span id="sphx-glr-auto-examples-plot-within-session-p300-py"></span><h1>Within Session P300<a class="headerlink" href="#within-session-p300" title="Permalink to this heading">#</a></h1>
+<p>This example shows how to perform a within session analysis on three different
+P300 datasets.</p>
+<p>We will compare two pipelines :</p>
+<ul class="simple">
+<li><p>Riemannian geometry</p></li>
+<li><p>XDAWN with Linear Discriminant Analysis</p></li>
+</ul>
+<p>We will use the P300 paradigm, which uses the AUC as metric.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro Rodrigues &lt;pedro.rodrigues01@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.Vectorizer.html#mne.decoding.Vectorizer" title="mne.decoding.Vectorizer" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">Vectorizer</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Xdawn</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a>
+</pre></div>
+</div>
+<p>getting rid of the warnings about the future</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipelines must be a dict of sklearn pipeline transformer.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+</pre></div>
+</div>
+<p>We have to do this because the classes are called ‘Target’ and ‘NonTarget’
+but the evaluation function uses a LabelEncoder, transforming them
+to 0 and 1</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels_dict</span></a> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Target&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;NonTarget&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;RG+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">XdawnCovariances</span></a><span class="p">(</span>
+        <span class="n">nfilter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels_dict</span></a><span class="p">[</span><span class="s2">&quot;Target&quot;</span><span class="p">]],</span> <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">,</span> <span class="n">xdawn_estimator</span><span class="o">=</span><span class="s2">&quot;scm&quot;</span>
+    <span class="p">),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(),</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;Xdw+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Xdawn</span></a><span class="p">(</span><span class="n">nfilter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;scm&quot;</span><span class="p">),</span> <a href="https://mne.tools/stable/generated/mne.decoding.Vectorizer.html#mne.decoding.Vectorizer" title="mne.decoding.Vectorizer" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">Vectorizer</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;lsqr&quot;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>We define the paradigm (P300) and use all three datasets available for it.
+The evaluation will return a DataFrame containing a single AUC score for
+each subject / session of the dataset, and for each pipeline.</p>
+<p>Results are saved into the database, so that if you add a new pipeline, it
+will not run again the evaluation unless a parameter has changed. Results can
+be overwritten if necessary.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">P300</span></a><span class="p">(</span><span class="n">resample</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
+<a href="../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_009</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+<a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-009-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-009-WithinSession:  50%|#####     | 1/2 [00:09&lt;00:09,  9.78s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  576 events (all good), 0 – 0.800781 s, baseline off, ~14.5 MB, data loaded,
+ &#39;Target&#39;: 96
+ &#39;NonTarget&#39;: 480&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-009-WithinSession: 100%|##########| 2/2 [00:19&lt;00:00,  9.62s/it]
+BNCI2014-009-WithinSession: 100%|##########| 2/2 [00:19&lt;00:00,  9.64s/it]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results to compare the two pipelines</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">stripplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span>
+    <span class="n">jitter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Set1&quot;</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_within_session_p300_001.png" srcset="../_images/sphx_glr_plot_within_session_p300_001.png" alt="plot within session p300" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  21.487 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  19 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-within-session-p300-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/39844aaaee383c2803c6ac93e64037d5/plot_within_session_p300.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_within_session_p300.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
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+<li class="toctree-l2"><a class="reference internal" href="plot_explore_paradigm.html">Explore Paradigm Object</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_fixed_interval_windows.html">Fixed interval windows processing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_within_session_p300.html">Within Session P300</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Within Session SSVEP</a></li>
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+</div>
+<section class="sphx-glr-example-title" id="within-session-ssvep">
+<span id="sphx-glr-auto-examples-plot-within-session-ssvep-py"></span><h1>Within Session SSVEP<a class="headerlink" href="#within-session-ssvep" title="Permalink to this heading">#</a></h1>
+<p>This Example show how to perform a within-session SSVEP analysis on the
+MAMEM dataset 3, using a CCA pipeline.</p>
+<p>The within-session evaluation assesses the performance of a classification
+pipeline using a 5-fold cross-validation. The reported metric (here, accuracy)
+is the average of all fold.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Sylvain Chevallier &lt;sylvain.chevallier@uvsq.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">MAMEM3</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">SSVEP</span></a>
+<span class="kn">from</span> <span class="nn">moabb.pipelines</span> <span class="kn">import</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_CCA</span></a>
+
+
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.simplefilter" title="warnings.simplefilter" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span></a><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">RuntimeWarning</span><span class="p">)</span>
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="loading-dataset">
+<h2>Loading Dataset<a class="headerlink" href="#loading-dataset" title="Permalink to this heading">#</a></h2>
+<p>Load 2 subjects of MAMEM3 dataset</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<a href="../generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">MAMEM3</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a>
+</pre></div>
+</div>
+</section>
+<section id="choose-paradigm">
+<h2>Choose Paradigm<a class="headerlink" href="#choose-paradigm" title="Permalink to this heading">#</a></h2>
+<p>We select the paradigm SSVEP, applying a bandpass filter (3-15 Hz) on
+the data and we keep only the first 3 classes, that is stimulation
+frequency of 6.66, 7.50 and 8.57 Hz.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">SSVEP</span></a><span class="p">(</span><span class="n">fmin</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-pipelines">
+<h2>Create Pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>Use a Canonical Correlation Analysis classifier</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">interval</span></a>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.used_events" title="moabb.paradigms.ssvep.BaseSSVEP.used_events" class="sphx-glr-backref-module-moabb-paradigms-ssvep sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">used_events</span></a><span class="p">(</span><a href="../generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a><span class="p">[</span><span class="s2">&quot;CCA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA" class="sphx-glr-backref-module-moabb-pipelines-classification sphx-glr-backref-type-py-class"><span class="n">SSVEP_CCA</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">interval</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freqs</span></a><span class="p">,</span> <span class="n">n_harmonics</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+</div>
+</section>
+<section id="get-data-optional">
+<h2>Get Data (optional)<a class="headerlink" href="#get-data-optional" title="Permalink to this heading">#</a></h2>
+<p>To get access to the EEG signals downloaded from the dataset, you could
+use <cite>dataset.get_data(subjects=[subject_id])</cite> to obtain the EEG under
+MNE format, stored in a dictionary of sessions and runs.
+Otherwise, <cite>paradigm.get_data(dataset=dataset, subjects=[subject_id])</cite>
+allows to obtain the EEG data in scikit format, the labels and the meta
+information. In <cite>paradigm.get_data</cite>, the EEG are preprocessed according
+to the paradigm requirement.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># sessions = dataset.get_data(subjects=[3])</span>
+<span class="c1"># X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[3])</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>The evaluation will return a DataFrame containing a single AUC score for
+each subject and pipeline.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># set to True if we want to overwrite cached results</span>
+
+<a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><a href="../generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">overwrite</span></a>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>MAMEM3-WithinSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+
+MAMEM3-WithinSession:  50%|#####     | 1/2 [00:02&lt;00:02,  2.16s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  5 events (all good), 1 – 4 s, baseline off, ~236 kB, data loaded,
+ &#39;6.66&#39;: 3
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 2&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  4 events (all good), 1 – 4 s, baseline off, ~194 kB, data loaded,
+ &#39;6.66&#39;: 1
+ &#39;7.50&#39;: 0
+ &#39;8.57&#39;: 3&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+No hdf5_path provided, models will not be saved.
+
+MAMEM3-WithinSession: 100%|##########| 2/2 [00:04&lt;00:00,  2.07s/it]
+MAMEM3-WithinSession: 100%|##########| 2/2 [00:04&lt;00:00,  2.08s/it]
+      score      time  samples subject  ... channels  n_sessions  dataset pipeline
+0  0.688889  0.048859     45.0       1  ...       14           1   MAMEM3      CCA
+1  0.266667  0.048093     45.0       3  ...       14           1   MAMEM3      CCA
+
+[2 rows x 9 columns]
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
+<p>Here we plot the results, indicating the score for each subject</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;session&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_within_session_ssvep_001.png" srcset="../_images/sphx_glr_plot_within_session_ssvep_001.png" alt="plot within session ssvep" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: xlabel=&#39;session&#39;, ylabel=&#39;score&#39;&gt;
+</pre></div>
+</div>
+<p>And the computation time in seconds</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;session&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;Reds&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Time (s)&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_plot_within_session_ssvep_002.png" srcset="../_images/sphx_glr_plot_within_session_ssvep_002.png" alt="plot within session ssvep" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.174 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-within-session-ssvep-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/09c597e028c1574e907e103b3db53254/plot_within_session_ssvep.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_within_session_ssvep.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/f3215cecaa532ec823443b1b0630d3fb/plot_within_session_ssvep.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_within_session_ssvep.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-dataset">Loading Dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choose-paradigm">Choose Paradigm</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipelines">Create Pipelines</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#get-data-optional">Get Data (optional)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
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+
+
+<!DOCTYPE html>
+
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="../_static/moabb_logo.png" type="image/png">
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+<div class="header-article-items header-article__inner">
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+    <li class="breadcrumb-item active" aria-current="page">Computation times</li>
+  </ul>
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+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
+<p><strong>16:10.695</strong> total execution time for <strong>auto_examples</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_DL.py</span></code>)</p></td>
+<td><p>09:03.512</p></td>
+<td><p>995.8 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_braindecode.py</span></code>)</p></td>
+<td><p>03:01.175</p></td>
+<td><p>581.9 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_benchmark_grid_search.html#sphx-glr-auto-examples-plot-benchmark-grid-search-py"><span class="std std-ref">Benchmarking with MOABB with Grid Search</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_grid_search.py</span></code>)</p></td>
+<td><p>01:34.763</p></td>
+<td><p>243.2 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_disk_cache.py</span></code>)</p></td>
+<td><p>00:27.185</p></td>
+<td><p>324.9 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_explore_paradigm.py</span></code>)</p></td>
+<td><p>00:23.021</p></td>
+<td><p>798.7 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_within_session_p300.py</span></code>)</p></td>
+<td><p>00:21.487</p></td>
+<td><p>19.4 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark.py</span></code>)</p></td>
+<td><p>00:20.819</p></td>
+<td><p>352.5 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_cross_session_multiple_datasets.py</span></code>)</p></td>
+<td><p>00:15.440</p></td>
+<td><p>315.7 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_braindecode.py</span></code>)</p></td>
+<td><p>00:14.669</p></td>
+<td><p>354.1 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_cross_session_motor_imagery.py</span></code>)</p></td>
+<td><p>00:10.019</p></td>
+<td><p>332.1 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_fixed_interval_windows.py</span></code>)</p></td>
+<td><p>00:07.223</p></td>
+<td><p>303.6 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_within_session_ssvep.py</span></code>)</p></td>
+<td><p>00:07.174</p></td>
+<td><p>9.9 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_cross_subject_ssvep.py</span></code>)</p></td>
+<td><p>00:04.207</p></td>
+<td><p>8.8 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="changing_download_directory.html#sphx-glr-auto-examples-changing-download-directory-py"><span class="std std-ref">Change Download Directory</span></a> (<code class="docutils literal notranslate"><span class="pre">changing_download_directory.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a> (<code class="docutils literal notranslate"><span class="pre">example_codecarbon.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="load_model.html#sphx-glr-auto-examples-load-model-py"><span class="std std-ref">Load Model (Scikit, Pytorch, Keras) with MOABB</span></a> (<code class="docutils literal notranslate"><span class="pre">load_model.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="noplot_bids_conversion.html#sphx-glr-auto-examples-noplot-bids-conversion-py"><span class="std std-ref">Convert a MOABB dataset to BIDS</span></a> (<code class="docutils literal notranslate"><span class="pre">noplot_bids_conversion.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="noplot_phmd_ml_spectrum.html#sphx-glr-auto-examples-noplot-phmd-ml-spectrum-py"><span class="std std-ref">Spectral analysis of the trials</span></a> (<code class="docutils literal notranslate"><span class="pre">noplot_phmd_ml_spectrum.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://neurotechx.github.io/moabb/auto_examples/noplot_vr_pc_p300_different_epoch_size.html#sphx-glr-auto-examples-noplot-vr-pc-p300-different-epoch-size-py" title="(in moabb v1.0.0)"><span class="xref std std-ref">sphx_glr_auto_examples_noplot_vr_pc_p300_different_epoch_size.py</span></a> (<code class="docutils literal notranslate"><span class="pre">noplot_vr_pc_p300_different_epoch_size.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+</tbody>
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diff --git a/docs/auto_tutorials/4_adding_a_dataset.html b/docs/auto_tutorials/4_adding_a_dataset.html
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Getting Started</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Tutorial 4: Creating a dataset class</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
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+              
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+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-tutorials-4-adding-a-dataset-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="tutorial-4-creating-a-dataset-class">
+<span id="sphx-glr-auto-tutorials-4-adding-a-dataset-py"></span><h1>Tutorial 4: Creating a dataset class<a class="headerlink" href="#tutorial-4-creating-a-dataset-class" title="Permalink to this heading">#</a></h1>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro L. C. Rodrigues, Sylvain Chevallier</span>
+<span class="c1">#</span>
+<span class="c1"># https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyriemann.classification</span> <span class="kn">import</span> <span class="n">MDM</span>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <span class="n">Covariances</span>
+<span class="kn">from</span> <span class="nn">scipy.io</span> <span class="kn">import</span> <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat" title="scipy.io.loadmat" class="sphx-glr-backref-module-scipy-io sphx-glr-backref-type-py-function"><span class="n">loadmat</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.savemat.html#scipy.io.savemat" title="scipy.io.savemat" class="sphx-glr-backref-module-scipy-io sphx-glr-backref-type-py-function"><span class="n">savemat</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <span class="n">download</span> <span class="k">as</span> <span class="n">dl</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets.base</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset" class="sphx-glr-backref-module-moabb-datasets-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BaseDataset</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LeftRightImagery</span></a>
+</pre></div>
+</div>
+<section id="creating-some-data">
+<h2>Creating some Data<a class="headerlink" href="#creating-some-data" title="Permalink to this heading">#</a></h2>
+<p>To illustrate the creation of a dataset class in MOABB, we first create an
+example dataset saved in .mat file. It contains a single fake recording on
+8 channels lasting for 150 seconds (sampling frequency 256 Hz). We have
+included the script that creates this dataset and have uploaded it online.
+The fake dataset is available on the
+<a class="reference external" href="https://sandbox.zenodo.org/record/369543">Zenodo website</a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">create_example_dataset</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a fake example for a dataset.&quot;&quot;&quot;</span>
+    <span class="n">sfreq</span> <span class="o">=</span> <span class="mi">256</span>
+    <span class="n">t_recording</span> <span class="o">=</span> <span class="mi">150</span>
+    <span class="n">t_trial</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># duration of a trial</span>
+    <span class="n">intertrial</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># time between end of a trial and the next one</span>
+    <span class="n">n_chan</span> <span class="o">=</span> <span class="mi">8</span>
+
+    <span class="n">x</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">((</span><span class="n">n_chan</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">t_recording</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">))</span>  <span class="c1"># electrodes + stimulus</span>
+    <span class="n">stim</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="n">t_recording</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span>
+    <span class="n">t_offset</span> <span class="o">=</span> <span class="mf">1.0</span>  <span class="c1"># offset where the trials start</span>
+    <span class="n">n_trials</span> <span class="o">=</span> <span class="mi">40</span>
+
+    <span class="n">rep</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">t_trial</span><span class="p">,</span> <span class="n">t_trial</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span>
+    <span class="n">signal</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sin.html#numpy.sin" title="numpy.sin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">/</span> <span class="n">t_trial</span> <span class="o">*</span> <span class="n">rep</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_trials</span><span class="p">):</span>
+        <span class="n">label</span> <span class="o">=</span> <span class="n">n</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span>  <span class="c1"># alternate between class 0 and class 1</span>
+        <span class="n">tn</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_offset</span> <span class="o">*</span> <span class="n">sfreq</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">t_trial</span> <span class="o">+</span> <span class="n">intertrial</span><span class="p">)</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span>
+        <span class="n">stim</span><span class="p">[</span><span class="n">tn</span><span class="p">]</span> <span class="o">=</span> <span class="n">label</span>
+        <span class="n">noise</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="n">n_chan</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
+        <span class="n">x</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">tn</span> <span class="p">:</span> <span class="p">(</span><span class="n">tn</span> <span class="o">+</span> <span class="n">t_trial</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)]</span> <span class="o">=</span> <span class="n">label</span> <span class="o">*</span> <span class="n">signal</span> <span class="o">+</span> <span class="n">noise</span>
+    <span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">stim</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">sfreq</span>
+
+
+<span class="c1"># Create the fake data</span>
+<span class="k">for</span> <span class="n">subject</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+    <span class="n">x</span><span class="p">,</span> <span class="n">fs</span> <span class="o">=</span> <span class="n">create_example_dataset</span><span class="p">()</span>
+    <span class="n">filename</span> <span class="o">=</span> <span class="s2">&quot;subject_&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">subject</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.mat&quot;</span>
+    <span class="n">mdict</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">mdict</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span>
+    <span class="n">mdict</span><span class="p">[</span><span class="s2">&quot;fs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs</span>
+    <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.savemat.html#scipy.io.savemat" title="scipy.io.savemat" class="sphx-glr-backref-module-scipy-io sphx-glr-backref-type-py-function"><span class="n">savemat</span></a><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">mdict</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="creating-a-dataset-class">
+<h2>Creating a Dataset Class<a class="headerlink" href="#creating-a-dataset-class" title="Permalink to this heading">#</a></h2>
+<p>We will create now a dataset class using the fake data simulated with the
+code from above. For this, we first need to import the right classes from
+MOABB:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">dl</span></code> is a very useful script that downloads automatically a dataset online
+if it is not yet available in the user’s computer. The script knows where
+to download the files because we create a global variable telling the URL
+where to fetch the data.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code> is the basic class that we overload to create our dataset.</p></li>
+</ul>
+<p>The global variable with the dataset’s URL should specify an online
+repository where all the files are stored.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExampleDataset_URL</span> <span class="o">=</span> <span class="s2">&quot;https://sandbox.zenodo.org/record/369543/files/&quot;</span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">ExampleDataset</span></code> needs to implement only 3 functions:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">__init__</span></code> for indicating the parameter of the dataset</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">_get_single_subject_data</span></code> to define how to process the data once they
+have been downloaded</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">data_path</span></code> to define how the data are downloaded.</p></li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">ExampleDataset</span><span class="p">(</span><a href="../generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset" class="sphx-glr-backref-module-moabb-datasets-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BaseDataset</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Dataset used to exemplify the creation of a dataset class in MOABB.</span>
+
+<span class="sd">    The data samples have been simulated and has no physiological</span>
+<span class="sd">    meaning whatsoever.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">subjects</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="n">sessions_per_subject</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+            <span class="n">events</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;left_hand&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;right_hand&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span>
+            <span class="n">code</span><span class="o">=</span><span class="s2">&quot;ExampleDataset&quot;</span><span class="p">,</span>
+            <span class="n">interval</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">],</span>
+            <span class="n">paradigm</span><span class="o">=</span><span class="s2">&quot;imagery&quot;</span><span class="p">,</span>
+            <span class="n">doi</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_single_subject_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subject</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return data for a single subject.&quot;&quot;&quot;</span>
+        <span class="n">file_path_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_path</span><span class="p">(</span><span class="n">subject</span><span class="p">)</span>
+
+        <span class="n">data</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat" title="scipy.io.loadmat" class="sphx-glr-backref-module-scipy-io sphx-glr-backref-type-py-function"><span class="n">loadmat</span></a><span class="p">(</span><span class="n">file_path_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">]</span>
+        <span class="n">fs</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;fs&quot;</span><span class="p">]</span>
+        <span class="n">ch_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ch&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">)]</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;stim&quot;</span><span class="p">]</span>
+        <span class="n">ch_types</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;eeg&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">)]</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;stim&quot;</span><span class="p">]</span>
+        <span class="n">info</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.create_info.html#mne.create_info" title="mne.create_info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">create_info</span></a><span class="p">(</span><span class="n">ch_names</span><span class="p">,</span> <span class="n">fs</span><span class="p">,</span> <span class="n">ch_types</span><span class="p">)</span>
+        <span class="n">raw</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.io.RawArray.html#mne.io.RawArray" title="mne.io.RawArray" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">info</span><span class="p">)</span>
+
+        <span class="n">sessions</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">sessions</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">sessions</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">][</span><span class="s2">&quot;0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">raw</span>
+        <span class="k">return</span> <span class="n">sessions</span>
+
+    <span class="k">def</span> <span class="nf">data_path</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">subject</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Download the data from one subject.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">subject</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">subject_list</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="p">(</span><span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid subject number&quot;</span><span class="p">))</span>
+
+        <span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{:s}</span><span class="s2">subject_0</span><span class="si">{:d}</span><span class="s2">.mat&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ExampleDataset_URL</span><span class="p">,</span> <span class="n">subject</span><span class="p">)</span>
+        <span class="n">path</span> <span class="o">=</span> <a href="../generated/moabb.datasets.download.data_dl.html#moabb.datasets.download.data_dl" title="moabb.datasets.download.data_dl" class="sphx-glr-backref-module-moabb-datasets-download sphx-glr-backref-type-py-function"><span class="n">dl</span><span class="o">.</span><span class="n">data_dl</span></a><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="s2">&quot;ExampleDataset&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">path</span><span class="p">]</span>  <span class="c1"># it has to return a list</span>
+</pre></div>
+</div>
+</section>
+<section id="using-the-exampledataset">
+<h2>Using the ExampleDataset<a class="headerlink" href="#using-the-exampledataset" title="Permalink to this heading">#</a></h2>
+<p>Now that the <cite>ExampleDataset</cite> is defined, it could be instantiated directly.
+The rest of the code follows the steps described in the previous tutorials.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">ExampleDataset</span><span class="p">()</span>
+
+<span class="n">paradigm</span> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">meta</span> <span class="o">=</span> <span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> <span class="n">subjects</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="n">evaluation</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <span class="n">paradigm</span><span class="o">=</span><span class="n">paradigm</span><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;newdataset&quot;</span>
+<span class="p">)</span>
+<span class="n">pipelines</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">pipelines</span><span class="p">[</span><span class="s2">&quot;MDM&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">Covariances</span><span class="p">(</span><span class="s2">&quot;oas&quot;</span><span class="p">),</span> <span class="n">MDM</span><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">))</span>
+<span class="n">scores</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">process</span><span class="p">(</span><span class="n">pipelines</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="pushing-on-moabb-github">
+<h2>Pushing on MOABB Github<a class="headerlink" href="#pushing-on-moabb-github" title="Permalink to this heading">#</a></h2>
+<p>If you want to make your dataset available to everyone, you could upload
+your data on public server (like Zenodo or Figshare) and signal that you
+want to add your dataset to MOABB in the  <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/1">dedicated issue</a>.  # noqa: E501
+You could then follow the instructions on <a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/master/CONTRIBUTING.md">how to contribute</a>  # noqa: E501</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-tutorials-4-adding-a-dataset-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/004be3e9d1a7fe3f376ebea76a062f80/4_adding_a_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">4_adding_a_dataset.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/f2d00b929c914067772799fd6f99fb75/4_adding_a_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">4_adding_a_dataset.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
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+        <p class="prev-next-title">Getting Started</p>
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+       title="next page">
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+        <p class="prev-next-title">Tutorial 5: Creating a dataset class</p>
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-some-data">Creating some Data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-a-dataset-class">Creating a Dataset Class</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#using-the-exampledataset">Using the ExampleDataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pushing-on-moabb-github">Pushing on MOABB Github</a></li>
+</ul>
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+            
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+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
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+    <link rel="moabb icon" sizes="180x180" href="../_static/moabb_logo.png" type="image/png">
+    <title>Getting Started &#8212; moabb 1.0.0-dev documentation</title>
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+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/noplot_vr_pc_p300_different_epoch_size.html">Changing epoch size in P300 VR dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_benchmark_grid_search.html">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html">Cross-Subject SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_disk_cache.html">Cache on disk intermediate data processing states</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_within_session_p300.html">Within Session P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html">Within Session SSVEP</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.BNCI2014_004.html">moabb.datasets.BNCI2014_004</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_VR.html">moabb.datasets.Cattan2019_VR</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Huebner2017.html">moabb.datasets.Huebner2017</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_ERP.html">moabb.datasets.Lee2019_ERP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Sosulski2019.html">moabb.datasets.Sosulski2019</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Nakanishi2015.html">moabb.datasets.Nakanishi2015</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Wang2016.html">moabb.datasets.Wang2016</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM1.html">moabb.datasets.MAMEM1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM2.html">moabb.datasets.MAMEM2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.MAMEM3.html">moabb.datasets.MAMEM3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Lee2019_SSVEP.html">moabb.datasets.Lee2019_SSVEP</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Thielen2021.html">moabb.datasets.Thielen2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP40.html">moabb.datasets.CastillosBurstVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosBurstVEP100.html">moabb.datasets.CastillosBurstVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP40.html">moabb.datasets.CastillosCVEP40</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.CastillosCVEP100.html">moabb.datasets.CastillosCVEP100</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.Cattan2019_PHMD.html">moabb.datasets.Cattan2019_PHMD</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.BaseDataset.html">moabb.datasets.base.BaseDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.base.CacheConfig.html">moabb.datasets.base.CacheConfig</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.fake.FakeVirtualRealityDataset.html">moabb.datasets.fake.FakeVirtualRealityDataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.find_intersecting_channels.html">moabb.datasets.utils.find_intersecting_channels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014a_Il.html">moabb.datasets.compound_dataset.BI2014a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Tutorial 5: Creating a dataset class</li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="tutorial-5-creating-a-dataset-class">
+<span id="sphx-glr-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"></span><h1>Tutorial 5: Creating a dataset class<a class="headerlink" href="#tutorial-5-creating-a-dataset-class" title="Permalink to this heading">#</a></h1>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Gregoire Cattan</span>
+<span class="c1">#</span>
+<span class="c1"># https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019</span>
+
+<span class="kn">from</span> <span class="nn">pyriemann.classification</span> <span class="kn">import</span> <span class="n">MDM</span>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <span class="n">ERPCovariances</span>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Cattan2019_VR</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets.braininvaders</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a" title="moabb.datasets.BI2014a" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BI2014a</span></a>
+<span class="kn">from</span> <span class="nn">moabb.datasets.compound_dataset</span> <span class="kn">import</span> <span class="n">CompoundDataset</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets.utils</span> <span class="kn">import</span> <span class="n">blocks_reps</span>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms.p300</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">P300</span></a>
+</pre></div>
+</div>
+<section id="initialization">
+<h2>Initialization<a class="headerlink" href="#initialization" title="Permalink to this heading">#</a></h2>
+<p>This tutorial illustrates how to use the CompoundDataset to:
+1) Select a few subjects/sessions/runs in an existing dataset
+2) Merge two CompoundDataset into a new one
+3) … and finally use this new dataset on a pipeline
+(this steps is not specific to CompoundDataset)</p>
+<p>Let’s define a paradigm and a pipeline for evaluation first.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">paradigm</span> <span class="o">=</span> <a href="../generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">P300</span></a><span class="p">()</span>
+<span class="n">pipelines</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">pipelines</span><span class="p">[</span><span class="s2">&quot;MDM&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">ERPCovariances</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;lwf&quot;</span><span class="p">),</span> <span class="n">MDM</span><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+</section>
+<section id="creation-a-selection-of-subject">
+<h2>Creation a selection of subject<a class="headerlink" href="#creation-a-selection-of-subject" title="Permalink to this heading">#</a></h2>
+<p>We are going to great two CompoundDataset, namely CustomDataset1 &amp;  2.
+A CompoundDataset accepts a subjects_list of subjects.
+It is a list of tuple. A tuple contains 4 values:
+- the original dataset
+- the subject number to select
+- the sessions. It can be:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>a session name (‘0’)</p></li>
+<li><p>a list of sessions ([‘0’, ‘1’])</p></li>
+<li><p><cite>None</cite> to select all the sessions attributed to a subject</p></li>
+</ul>
+</div></blockquote>
+<ul class="simple">
+<li><p>the runs. As for sessions, it can be a single run name, a list or <cite>None`</cite> (to select all runs).</p></li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CustomDataset1</span><span class="p">(</span><span class="n">CompoundDataset</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">biVR</span> <span class="o">=</span> <a href="../generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Cattan2019_VR</span></a><span class="p">(</span><span class="n">virtual_reality</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">screen_display</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">runs</span> <span class="o">=</span> <span class="n">blocks_reps</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">biVR</span><span class="o">.</span><span class="n">n_repetitions</span><span class="p">)</span>
+        <span class="n">subjects_list</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">(</span><span class="n">biVR</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;0VR&quot;</span><span class="p">,</span> <span class="n">runs</span><span class="p">),</span>
+            <span class="p">(</span><span class="n">biVR</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;0VR&quot;</span><span class="p">,</span> <span class="n">runs</span><span class="p">),</span>
+        <span class="p">]</span>
+        <span class="n">CompoundDataset</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">subjects_list</span><span class="o">=</span><span class="n">subjects_list</span><span class="p">,</span>
+            <span class="n">code</span><span class="o">=</span><span class="s2">&quot;CustomDataset1&quot;</span><span class="p">,</span>
+            <span class="n">interval</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
+        <span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">CustomDataset2</span><span class="p">(</span><span class="n">CompoundDataset</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">bi2014</span> <span class="o">=</span> <a href="../generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a" title="moabb.datasets.BI2014a" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BI2014a</span></a><span class="p">()</span>
+        <span class="n">subjects_list</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">(</span><span class="n">bi2014</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
+            <span class="p">(</span><span class="n">bi2014</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
+        <span class="p">]</span>
+        <span class="n">CompoundDataset</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">subjects_list</span><span class="o">=</span><span class="n">subjects_list</span><span class="p">,</span>
+            <span class="n">code</span><span class="o">=</span><span class="s2">&quot;CustomDataset2&quot;</span><span class="p">,</span>
+            <span class="n">interval</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
+        <span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="merging-the-datasets">
+<h2>Merging the datasets<a class="headerlink" href="#merging-the-datasets" title="Permalink to this heading">#</a></h2>
+<p>We are now going to merge the two CompoundDataset into a single one.
+The implementation is straight forward. Instead of providing a list of subjects,
+you should provide a list of CompoundDataset.
+subjects_list = [CustomDataset1(), CustomDataset2()]</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CustomDataset3</span><span class="p">(</span><span class="n">CompoundDataset</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">subjects_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">CustomDataset1</span><span class="p">(),</span> <span class="n">CustomDataset2</span><span class="p">()]</span>
+        <span class="n">CompoundDataset</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">subjects_list</span><span class="o">=</span><span class="n">subjects_list</span><span class="p">,</span>
+            <span class="n">code</span><span class="o">=</span><span class="s2">&quot;CustomDataset3&quot;</span><span class="p">,</span>
+            <span class="n">interval</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
+        <span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluate-and-display">
+<h2>Evaluate and display<a class="headerlink" href="#evaluate-and-display" title="Permalink to this heading">#</a></h2>
+<p>Let’s use a WithinSessionEvaluation to evaluate our new dataset.
+If you already new how to do this, nothing changed:
+The CompoundDataset can be used as a <cite>normal</cite> dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span><span class="n">CustomDataset3</span><span class="p">()]</span>
+<span class="n">evaluation</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <span class="n">paradigm</span><span class="o">=</span><span class="n">paradigm</span><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><span class="n">datasets</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;newdataset&quot;</span>
+<span class="p">)</span>
+<span class="n">scores</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">process</span><span class="p">(</span><span class="n">pipelines</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/3f1909c085169067146e835f0f34191d/noplot_tutorial_5_build_a_custom_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">noplot_tutorial_5_build_a_custom_dataset.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/4819137c12db5666618aa0e7e8603bce/noplot_tutorial_5_build_a_custom_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">noplot_tutorial_5_build_a_custom_dataset.ipynb</span></code></a></p>
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+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#initialization">Initialization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creation-a-selection-of-subject">Creation a selection of subject</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#merging-the-datasets">Merging the datasets</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluate-and-display">Evaluate and display</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_benchmark_grid_search.html">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_hash.html">moabb.datasets.download.fs_get_file_hash</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_id.html">moabb.datasets.download.fs_get_file_id</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.download.fs_get_file_name.html">moabb.datasets.download.fs_get_file_name</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.dataset_search.html">moabb.datasets.utils.dataset_search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.utils.find_intersecting_channels.html">moabb.datasets.utils.find_intersecting_channels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014a_Il.html">moabb.datasets.compound_dataset.BI2014a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2014b_Il.html">moabb.datasets.compound_dataset.BI2014b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015a_Il.html">moabb.datasets.compound_dataset.BI2015a_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI2015b_Il.html">moabb.datasets.compound_dataset.BI2015b_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html">moabb.datasets.compound_dataset.Cattan2019_VR_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.datasets.compound_dataset.BI_Il.html">moabb.datasets.compound_dataset.BI_Il</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.WithinSessionEvaluation.html">moabb.evaluations.WithinSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSessionEvaluation.html">moabb.evaluations.CrossSessionEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.CrossSubjectEvaluation.html">moabb.evaluations.CrossSubjectEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.evaluations.base.BaseEvaluation.html">moabb.evaluations.base.BaseEvaluation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.MotorImagery.html">moabb.paradigms.MotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.LeftRightImagery.html">moabb.paradigms.LeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankLeftRightImagery.html">moabb.paradigms.FilterBankLeftRightImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankMotorImagery.html">moabb.paradigms.FilterBankMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
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+  <div id="rtd-footer-container"></div>
+
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+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
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+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
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+      <a href="../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../overview.html" class="nav-link">Documentation overview</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Getting Started</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Tutorial 0: Getting Started</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-tutorials-plot-getting-started-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="tutorial-0-getting-started">
+<span id="sphx-glr-auto-tutorials-plot-getting-started-py"></span><h1>Tutorial 0: Getting Started<a class="headerlink" href="#tutorial-0-getting-started" title="Permalink to this heading">#</a></h1>
+<p>This tutorial takes you through a basic working example of how to use this
+codebase, including all the different components, up to the results
+generation. If you’d like to know about the statistics and plotting, see the
+next tutorial.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Vinay Jayaram &lt;vinayjayaram13@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="introduction">
+<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h2>
+<p>To use the codebase you need an evaluation and a paradigm, some algorithms,
+and a list of datasets to run it all on. You can find those in the following
+submodules; detailed tutorials are given for each of them.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class"><span class="n">SVC</span></a>
+</pre></div>
+</div>
+<p>If you would like to specify the logging level when it is running, you can
+use the standard python logging commands through the top-level moabb module</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <span class="n">utils</span>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+<span class="kn">from</span> <span class="nn">moabb.pipelines.features</span> <span class="kn">import</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance" title="moabb.pipelines.features.LogVariance" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class"><span class="n">LogVariance</span></a>
+</pre></div>
+</div>
+<p>In order to create pipelines within a script, you will likely need at least
+the make_pipeline function. They can also be specified via a .yml file. Here
+we will make a couple pipelines just for convenience</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-pipelines">
+<h2>Create pipelines<a class="headerlink" href="#create-pipelines" title="Permalink to this heading">#</a></h2>
+<p>We create two pipelines: channel-wise log variance followed by LDA, and
+channel-wise log variance followed by a cross-validated SVM (note that a
+cross-validation via scikit-learn cannot be described in a .yml file). For
+later in the process, the pipelines need to be in a dictionary where the key
+is the name of the pipeline and the value is the Pipeline object</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;AM+LDA&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance" title="moabb.pipelines.features.LogVariance" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class"><span class="n">LogVariance</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parameters</span></a> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;C&quot;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.logspace.html#numpy.logspace" title="numpy.logspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">logspace</span></a><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)}</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class"><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">),</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parameters</span></a><span class="p">)</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipe</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance" title="moabb.pipelines.features.LogVariance" class="sphx-glr-backref-module-moabb-pipelines-features sphx-glr-backref-type-py-class"><span class="n">LogVariance</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;AM+SVM&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipe</span></a>
+</pre></div>
+</div>
+</section>
+<section id="datasets">
+<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this heading">#</a></h2>
+<p>Datasets can be specified in many ways: Each paradigm has a property
+‘datasets’ which returns the datasets that are appropriate for that paradigm</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span><span class="o">.</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;moabb.datasets.bnci.BNCI2014_001 object at 0x7fce0463cf40&gt;, &lt;moabb.datasets.bnci.BNCI2014_004 object at 0x7fce0463ce80&gt;, &lt;moabb.datasets.gigadb.Cho2017 object at 0x7fce0463c1c0&gt;, &lt;moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7fce0463c190&gt;, &lt;moabb.datasets.Lee2019.Lee2019_MI object at 0x7fce0463cd00&gt;, &lt;moabb.datasets.physionet_mi.PhysionetMI object at 0x7fce0463c700&gt;, &lt;moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7fce0463cfd0&gt;, &lt;moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7fce0463c5e0&gt;, &lt;moabb.datasets.Weibo2014.Weibo2014 object at 0x7fce0463c370&gt;, &lt;moabb.datasets.Zhou2016.Zhou2016 object at 0x7fce0463ca90&gt;]
+</pre></div>
+</div>
+<p>Or you can run a search through the available datasets:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../generated/moabb.datasets.utils.dataset_search.html#moabb.datasets.utils.dataset_search" title="moabb.datasets.utils.dataset_search" class="sphx-glr-backref-module-moabb-datasets-utils sphx-glr-backref-type-py-function"><span class="n">utils</span><span class="o">.</span><span class="n">dataset_search</span></a><span class="p">(</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><span class="s2">&quot;imagery&quot;</span><span class="p">,</span> <span class="n">min_subjects</span><span class="o">=</span><span class="mi">6</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;moabb.datasets.alex_mi.AlexMI object at 0x7fce0463c7f0&gt;, &lt;moabb.datasets.bnci.BNCI2014_001 object at 0x7fce0463ce80&gt;, &lt;moabb.datasets.bnci.BNCI2014_002 object at 0x7fce0463cf10&gt;, &lt;moabb.datasets.bnci.BNCI2014_004 object at 0x7fce0463c9a0&gt;, &lt;moabb.datasets.bnci.BNCI2015_001 object at 0x7fce0463c9d0&gt;, &lt;moabb.datasets.bnci.BNCI2015_004 object at 0x7fce0463c310&gt;, &lt;moabb.datasets.gigadb.Cho2017 object at 0x7fce0463cbb0&gt;, &lt;moabb.datasets.fake.FakeDataset object at 0x7fce0463ce20&gt;, &lt;moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7fce0463c850&gt;, &lt;moabb.datasets.Lee2019.Lee2019_MI object at 0x7fce0463c100&gt;, &lt;moabb.datasets.upper_limb.Ofner2017 object at 0x7fce0463cf40&gt;, &lt;moabb.datasets.physionet_mi.PhysionetMI object at 0x7fce0463c970&gt;, &lt;moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7fce0463c5b0&gt;, &lt;moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7fce0463c280&gt;, &lt;moabb.datasets.Weibo2014.Weibo2014 object at 0x7fce0463c940&gt;]
+</pre></div>
+</div>
+<p>Or you can simply make your own list (which we do here due to computational
+constraints)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="paradigm">
+<h2>Paradigm<a class="headerlink" href="#paradigm" title="Permalink to this heading">#</a></h2>
+<p>Paradigms define the events, epoch time, bandpass, and other preprocessing
+parameters. They have defaults that you can read in the documentation, or you
+can simply set them as we do here. A single paradigm defines a method for
+going from continuous data to trial data of a fixed size. To learn more look
+at the tutorial Exploring Paradigms</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a> <span class="o">=</span> <span class="mi">8</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a> <span class="o">=</span> <span class="mi">35</span>
+<a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fmax</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>An evaluation defines how the training and test sets are chosen. This could
+be cross-validated within a single recording, or across days, or sessions, or
+subjects. This also is the correct place to specify multiple threads.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">CrossSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">&quot;examples&quot;</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00&lt;?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession:  50%|#####     | 1/2 [00:03&lt;00:03,  3.36s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs &lt;Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
+ &#39;left_hand&#39;: 12
+ &#39;right_hand&#39;: 12&gt;
+  warn(f&quot;warnEpochs {epochs}&quot;)
+
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:06&lt;00:00,  3.33s/it]
+BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:06&lt;00:00,  3.34s/it]
+</pre></div>
+</div>
+<p>Results are returned as a pandas DataFrame, and from here you can do as you
+want with them</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>      score      time  samples  ... n_sessions       dataset  pipeline
+0  0.797068  0.149245    144.0  ...          2  BNCI2014-001    AM+SVM
+1  0.773920  0.139139    144.0  ...          2  BNCI2014-001    AM+SVM
+2  0.550733  0.247906    144.0  ...          2  BNCI2014-001    AM+SVM
+3  0.471451  0.168360    144.0  ...          2  BNCI2014-001    AM+SVM
+4  0.786458  0.024171    144.0  ...          2  BNCI2014-001    AM+LDA
+
+[5 rows x 9 columns]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  9.886 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  306 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-tutorials-plot-getting-started-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/55cdcc1a57595324be0a4f77a5294187/plot_Getting_Started.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_Getting_Started.py</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../_downloads/c82f3928cb70181ccb714f9ce973e8e7/plot_Getting_Started.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_Getting_Started.ipynb</span></code></a></p>
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+                      </a>
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+<span id="sphx-glr-auto-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
+<p><strong>01:41.204</strong> total execution time for <strong>auto_tutorials</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a> (<code class="docutils literal notranslate"><span class="pre">tutorial_3_benchmarking_multiple_pipelines.py</span></code>)</p></td>
+<td><p>00:55.412</p></td>
+<td><p>326.1 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a> (<code class="docutils literal notranslate"><span class="pre">tutorial_1_simple_example_motor_imagery.py</span></code>)</p></td>
+<td><p>00:21.492</p></td>
+<td><p>580.9 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">tutorial_2_using_mulitple_datasets.py</span></code>)</p></td>
+<td><p>00:14.414</p></td>
+<td><p>207.7 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_Getting_Started.py</span></code>)</p></td>
+<td><p>00:09.886</p></td>
+<td><p>305.8 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="4_adding_a_dataset.html#sphx-glr-auto-tutorials-4-adding-a-dataset-py"><span class="std std-ref">Tutorial 4: Creating a dataset class</span></a> (<code class="docutils literal notranslate"><span class="pre">4_adding_a_dataset.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="noplot_tutorial_5_build_a_custom_dataset.html#sphx-glr-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"><span class="std std-ref">Tutorial 5: Creating a dataset class</span></a> (<code class="docutils literal notranslate"><span class="pre">noplot_tutorial_5_build_a_custom_dataset.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
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diff --git a/docs/auto_tutorials/tutorial_1_simple_example_motor_imagery.html b/docs/auto_tutorials/tutorial_1_simple_example_motor_imagery.html
new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/noplot_bids_conversion.html">Convert a MOABB dataset to BIDS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/noplot_phmd_ml_spectrum.html">Spectral analysis of the trials</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_benchmark_grid_search.html">Benchmarking with MOABB with Grid Search</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_braindecode.html">Cross-session motor imagery with deep learning EEGNet v4 model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html">Cross-Session Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html">Cross-Session on Multiple Datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html">Cross-Subject SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_disk_cache.html">Cache on disk intermediate data processing states</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html">Explore Paradigm Object</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html">Fixed interval windows processing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_within_session_p300.html">Within Session P300</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Tutorial 1: Simple Motor Imagery</li>
+  </ul>
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+              
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+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="tutorial-1-simple-motor-imagery">
+<span id="sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"></span><h1>Tutorial 1: Simple Motor Imagery<a class="headerlink" href="#tutorial-1-simple-motor-imagery" title="Permalink to this heading">#</a></h1>
+<p>In this example, we will go through all the steps to make a simple BCI
+classification task, downloading a dataset and using a standard classifier. We
+choose the dataset 2a from BCI Competition IV, a motor imagery task. We will
+use a CSP to enhance the signal-to-noise ratio of the EEG epochs and a LDA to
+classify these signals.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro L. C. Rodrigues, Sylvain Chevallier</span>
+<span class="c1">#</span>
+<span class="c1"># https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.filterwarnings" title="warnings.filterwarnings" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span></a><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="instantiating-dataset">
+<h2>Instantiating Dataset<a class="headerlink" href="#instantiating-dataset" title="Permalink to this heading">#</a></h2>
+<p>The first thing to do is to instantiate the dataset that we want to analyze.
+MOABB has a list of many different datasets, each one containing all the
+necessary information for describing them, such as the number of subjects,
+size of trials, names of classes, etc.</p>
+<p>The dataset class has methods for:</p>
+<ul class="simple">
+<li><p>downloading its files from some online source (e.g. Zenodo)</p></li>
+<li><p>importing the data from the files in whatever extension they might be
+(like .mat, .gdf, etc.) and instantiate a Raw object from the MNE package</p></li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="accessing-eeg-recording">
+<h2>Accessing EEG Recording<a class="headerlink" href="#accessing-eeg-recording" title="Permalink to this heading">#</a></h2>
+<p>As an example, we may access the EEG recording from a given session and a
+given run as follows:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sessions</span></a> <span class="o">=</span> <a href="../generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset.get_data" title="moabb.datasets.base.BaseDataset.get_data" class="sphx-glr-backref-module-moabb-datasets-base sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><span class="n">subjects</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>This returns a MNE Raw object that can be manipulated. This might be enough
+for some users, since the pre-processing and epoching steps can be easily
+done via MNE. However, to conduct an assessment of several classifiers on
+multiple subjects, MOABB ends up being a more appropriate option.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject</span></a> <span class="o">=</span> <span class="mi">1</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">session_name</span></a> <span class="o">=</span> <span class="s2">&quot;0train&quot;</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">run_name</span></a> <span class="o">=</span> <span class="s2">&quot;0&quot;</span>
+<a href="https://mne.tools/stable/generated/mne.io.RawArray.html#mne.io.RawArray" title="mne.io.RawArray" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">raw</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sessions</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject</span></a><span class="p">][</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">session_name</span></a><span class="p">][</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">run_name</span></a><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="choosing-a-paradigm">
+<h2>Choosing a Paradigm<a class="headerlink" href="#choosing-a-paradigm" title="Permalink to this heading">#</a></h2>
+<p>Once we have instantiated a dataset, we have to choose a paradigm. This
+object is responsible for filtering the data, epoching it, and extracting
+the labels for each epoch. Note that each dataset comes with the names of
+the paradigms to which it might be associated. It would not make sense to
+process a P300 dataset with a MI paradigm object.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">paradigm</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>imagery
+</pre></div>
+</div>
+<p>For the example below, we will consider the paradigm associated to
+left-hand/right-hand motor imagery task, but there are other options in
+MOABB for motor imagery, P300 or SSVEP.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>We may check the list of all datasets available in MOABB for using with this
+paradigm (note that BNCI2014_001 is in it)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" title="moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" class="sphx-glr-backref-module-moabb-paradigms-motor_imagery sphx-glr-backref-type-py-property"><span class="n">paradigm</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;moabb.datasets.bnci.BNCI2014_001 object at 0x7fcdfc9c0fa0&gt;, &lt;moabb.datasets.bnci.BNCI2014_004 object at 0x7fcdfc9c0070&gt;, &lt;moabb.datasets.gigadb.Cho2017 object at 0x7fcdfc9c0130&gt;, &lt;moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7fcdf4d4aa30&gt;, &lt;moabb.datasets.Lee2019.Lee2019_MI object at 0x7fcdfc9c0340&gt;, &lt;moabb.datasets.physionet_mi.PhysionetMI object at 0x7fcdfc9c09d0&gt;, &lt;moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7fcdfc9c0d00&gt;, &lt;moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7fcdfc9c0310&gt;, &lt;moabb.datasets.Weibo2014.Weibo2014 object at 0x7fcdfc9c05b0&gt;, &lt;moabb.datasets.Zhou2016.Zhou2016 object at 0x7fcdfc9c04c0&gt;]
+</pre></div>
+</div>
+<p>The data from a list of subjects could be preprocessed and return as a 3D
+numpy array <cite>X</cite>, follow a scikit-like format with the associated <cite>labels</cite>.
+The <cite>meta</cite> object contains all information regarding the subject, the
+session and the run associated to each trial.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">,</span> <span class="n">meta</span> <span class="o">=</span> <a href="https://neurotechx.github.io/moabb/generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data" class="sphx-glr-backref-module-moabb-paradigms-base sphx-glr-backref-type-py-method"><span class="n">paradigm</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">(</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">=</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">subjects</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="create-pipeline">
+<h2>Create Pipeline<a class="headerlink" href="#create-pipeline" title="Permalink to this heading">#</a></h2>
+<p>Our goal is to evaluate the performance of a given classification pipeline
+(or several of them) when it is applied to the epochs from the previously
+chosen dataset. We will consider a very simple classification pipeline in
+which the dimension of the epochs are reduced via a CSP step and then
+classified via a linear discriminant analysis.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
+<p>To evaluate the score of this pipeline, we use the <cite>evaluation</cite> class. When
+instantiating it, we say which paradigm we want to consider, a list with the
+datasets to analyze, and whether the scores should be recalculated each time
+we run the evaluation or if MOABB should create a cache file.</p>
+<p>Note that there are different ways of evaluating a classifier; in this
+example, we choose <cite>WithinSessionEvaluation</cite>, which consists of doing a
+cross-validation procedure where the training and testing partitions are from
+the same recording session of the dataset. We could have used
+<cite>CrossSessionEvaluation</cite>, which takes all but one session as training
+partition and the remaining one as testing partition.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span>
+    <span class="n">datasets</span><span class="o">=</span><span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">],</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">hdf5_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We obtain the results in the form of a pandas dataframe</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">({</span><span class="s2">&quot;csp+lda&quot;</span><span class="p">:</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a><span class="p">})</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-WithinSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession:  33%|###3      | 1/3 [00:04&lt;00:09,  4.73s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession:  67%|######6   | 2/3 [00:09&lt;00:04,  4.52s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession: 100%|##########| 3/3 [00:13&lt;00:00,  4.68s/it]
+BNCI2014-001-WithinSession: 100%|##########| 3/3 [00:13&lt;00:00,  4.66s/it]
+</pre></div>
+</div>
+<p>The results are stored in locally, to avoid recomputing the results each time.
+It is saved in <cite>hdf5_path</cite> if defined or in ~/mne_data/results  otherwise.
+To export the results in CSV:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;./results_part2-1.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>To load previously obtained results saved in CSV</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./results_part2-1.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="plotting-results">
+<h2>Plotting Results<a class="headerlink" href="#plotting-results" title="Permalink to this heading">#</a></h2>
+<p>We create a figure with the seaborn package comparing the classification
+score for each subject on each session. Note that the ‘subject’ field from
+the <cite>results</cite> is given in terms of integers, but seaborn accepts only
+strings for its labeling. This is why we create the field ‘subj’.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;subj&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;subject&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;subj&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;session&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;h&quot;</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+<span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_001.png" srcset="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_001.png" alt="tutorial 1 simple example motor imagery" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  21.492 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  581 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-tutorials-tutorial-1-simple-example-motor-imagery-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/b446bb6bcfa12c42d029c4259163f668/tutorial_1_simple_example_motor_imagery.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tutorial_1_simple_example_motor_imagery.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/9bb96dc7985cb9e3189360966fe90306/tutorial_1_simple_example_motor_imagery.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tutorial_1_simple_example_motor_imagery.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#instantiating-dataset">Instantiating Dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#accessing-eeg-recording">Accessing EEG Recording</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choosing-a-paradigm">Choosing a Paradigm</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pipeline">Create Pipeline</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SinglePass.html">moabb.paradigms.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.P300.html">moabb.paradigms.P300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.SSVEP.html">moabb.paradigms.SSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankSSVEP.html">moabb.paradigms.FilterBankSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.CVEP.html">moabb.paradigms.CVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankCVEP.html">moabb.paradigms.FilterBankCVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FixedIntervalWindowsProcessing.html">moabb.paradigms.FixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html">moabb.paradigms.motor_imagery.BaseMotorImagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.SinglePass.html">moabb.paradigms.motor_imagery.SinglePass</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.motor_imagery.FilterBank.html">moabb.paradigms.motor_imagery.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.p300.BaseP300.html">moabb.paradigms.p300.BaseP300</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.ssvep.BaseSSVEP.html">moabb.paradigms.ssvep.BaseSSVEP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html">moabb.paradigms.BaseFixedIntervalWindowsProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseParadigm.html">moabb.paradigms.base.BaseParadigm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.paradigms.base.BaseProcessing.html">moabb.paradigms.base.BaseProcessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.LogVariance.html">moabb.pipelines.features.LogVariance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.FM.html">moabb.pipelines.features.FM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.ExtendedSSVEPSignal.html">moabb.pipelines.features.ExtendedSSVEPSignal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.AugmentedDataset.html">moabb.pipelines.features.AugmentedDataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.features.StandardScaler_Epoch.html">moabb.pipelines.features.StandardScaler_Epoch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.csp.TRCSP.html">moabb.pipelines.csp.TRCSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
+</ul>
+</div>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Getting Started</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Tutorial 2: Using multiple datasets</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
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+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="tutorial-2-using-multiple-datasets">
+<span id="sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"></span><h1>Tutorial 2: Using multiple datasets<a class="headerlink" href="#tutorial-2-using-multiple-datasets" title="Permalink to this heading">#</a></h1>
+<p>We extend the previous example to a case where we want to analyze the score of
+a classifier with three different MI datasets instead of just one. As before,
+we begin by importing all relevant libraries.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro L. C. Rodrigues, Sylvain Chevallier</span>
+<span class="c1">#</span>
+<span class="c1"># https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;CRITICAL&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.filterwarnings" title="warnings.filterwarnings" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span></a><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="initializing-datasets">
+<h2>Initializing Datasets<a class="headerlink" href="#initializing-datasets" title="Permalink to this heading">#</a></h2>
+<p>We instantiate the two different datasets that follow the MI paradigm
+(with left-hand/right-hand classes) but were recorded with different number
+of electrodes, different number of trials, etc.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a><span class="p">(),</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">()]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<span class="k">for</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a>
+</pre></div>
+</div>
+<p>The following lines go exactly as in the previous example, where we end up
+obtaining a pandas dataframe containing the results of the evaluation. We
+could set <cite>overwrite</cite> to False to cache the results, avoiding to restart all
+the evaluation from scratch if a problem occurs.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">({</span><span class="s2">&quot;csp+lda&quot;</span><span class="p">:</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipeline</span></a><span class="p">})</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Zhou2016-WithinSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession:  33%|###3      | 1/3 [00:04&lt;00:09,  4.72s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession:  67%|######6   | 2/3 [00:08&lt;00:04,  4.26s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession: 100%|##########| 3/3 [00:12&lt;00:00,  4.12s/it]
+Zhou2016-WithinSession: 100%|##########| 3/3 [00:12&lt;00:00,  4.20s/it]
+
+BNCI2014-001-WithinSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]
+BNCI2014-001-WithinSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]
+</pre></div>
+</div>
+</section>
+<section id="plotting-results">
+<h2>Plotting Results<a class="headerlink" href="#plotting-results" title="Permalink to this heading">#</a></h2>
+<p>We plot the results using the seaborn library. Note how easy it
+is to plot the results from the three datasets with just one line.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;subj&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">resi</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">resi</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;subject&quot;</span><span class="p">]]</span>
+<span class="n">g</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span>
+    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;subj&quot;</span><span class="p">,</span>
+    <span class="n">col</span><span class="o">=</span><span class="s2">&quot;dataset&quot;</span><span class="p">,</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;h&quot;</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_001.png" srcset="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_001.png" alt="dataset = Zhou2016, dataset = BNCI2014-001" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  14.414 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  208 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-tutorials-tutorial-2-using-mulitple-datasets-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/a1721df1b7d19e3e027162c49fa733d4/tutorial_2_using_mulitple_datasets.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tutorial_2_using_mulitple_datasets.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/4ecaaa7a22e7ba0d94b874867e072727/tutorial_2_using_mulitple_datasets.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tutorial_2_using_mulitple_datasets.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_CCA.html">moabb.pipelines.classification.SSVEP_CCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_TRCA.html">moabb.pipelines.classification.SSVEP_TRCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.classification.SSVEP_MsetCCA.html">moabb.pipelines.classification.SSVEP_MsetCCA</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html">moabb.pipelines.deep_learning.KerasDeepConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGITNet.html">moabb.pipelines.deep_learning.KerasEEGITNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html">moabb.pipelines.deep_learning.KerasEEGNet_8_2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGNeX.html">moabb.pipelines.deep_learning.KerasEEGNeX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html">moabb.pipelines.deep_learning.KerasEEGTCNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html">moabb.pipelines.deep_learning.KerasShallowConvNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.create_pipeline_from_config.html">moabb.pipelines.utils.create_pipeline_from_config</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils.FilterBank.html">moabb.pipelines.utils.FilterBank</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet.html">moabb.pipelines.utils_deep_model.EEGNet</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html">moabb.pipelines.utils_deep_model.EEGNet_TC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_deep_model.TCN_block.html">moabb.pipelines.utils_deep_model.TCN_block</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html">moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html">moabb.pipelines.utils_pytorch.InputShapeSetterEEG</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.score_plot.html">moabb.analysis.plotting.score_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.paired_plot.html">moabb.analysis.plotting.paired_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.summary_plot.html">moabb.analysis.plotting.summary_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.plotting.meta_analysis_plot.html">moabb.analysis.plotting.meta_analysis_plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.find_significant_differences.html">moabb.analysis.meta_analysis.find_significant_differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html">moabb.analysis.meta_analysis.compute_dataset_statistics</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_effects.html">moabb.analysis.meta_analysis.combine_effects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.combine_pvalues.html">moabb.analysis.meta_analysis.combine_pvalues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.analysis.meta_analysis.collapse_session_scores.html">moabb.analysis.meta_analysis.collapse_session_scores</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.benchmark.html">moabb.benchmark</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_log_level.html">moabb.set_log_level</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.setup_seed.html">moabb.setup_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.set_download_dir.html">moabb.set_download_dir</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/moabb.make_process_pipelines.html">moabb.make_process_pipelines</a></li>
+</ul>
+</li>
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+    <li class="breadcrumb-item active" aria-current="page">Tutorial 3: Benchmarking multiple pipelines</li>
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+                  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p>Click <a class="reference internal" href="#sphx-glr-download-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">here</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="tutorial-3-benchmarking-multiple-pipelines">
+<span id="sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"></span><h1>Tutorial 3: Benchmarking multiple pipelines<a class="headerlink" href="#tutorial-3-benchmarking-multiple-pipelines" title="Permalink to this heading">#</a></h1>
+<p>In this last part, we extend the previous example by assessing the
+classification score of not one but three classification pipelines.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pedro L. C. Rodrigues, Sylvain Chevallier</span>
+<span class="c1">#</span>
+<span class="c1"># https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">mne.decoding</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.classification</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">MDM</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.estimation</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a>
+<span class="kn">from</span> <span class="nn">pyriemann.tangentspace</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class"><span class="n">SVC</span></a>
+
+<span class="kn">import</span> <span class="nn">moabb</span>
+<span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">,</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a>
+<span class="kn">from</span> <span class="nn">moabb.evaluations</span> <span class="kn">import</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a>
+<span class="kn">from</span> <span class="nn">moabb.paradigms</span> <span class="kn">import</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a>
+
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;CRITICAL&quot;</span><span class="p">)</span>
+<a href="../generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level" class="sphx-glr-backref-module-moabb sphx-glr-backref-type-py-function"><span class="n">moabb</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s2">&quot;info&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/warnings.html#warnings.filterwarnings" title="warnings.filterwarnings" class="sphx-glr-backref-module-warnings sphx-glr-backref-type-py-function"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span></a><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="creating-pipelines">
+<h2>Creating Pipelines<a class="headerlink" href="#creating-pipelines" title="Permalink to this heading">#</a></h2>
+<p>We instantiate the three different classiciation pipelines to be considered
+in the analysis. The object that gathers each pipeline is a dictionary. The
+first pipeline is the CSP+LDA that we have seen in the previous parts. The
+other two pipelines rely on Riemannian geometry, using an SVM classification
+in the tangent space of the covariance matrices estimated from the EEG or a
+MDM classifier that works directly on covariance matrices.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a> <span class="o">=</span> <span class="p">{}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;csp+lda&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.decoding.CSP.html#mne.decoding.CSP" title="mne.decoding.CSP" class="sphx-glr-backref-module-mne-decoding sphx-glr-backref-type-py-class"><span class="n">CSP</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class"><span class="n">LDA</span></a><span class="p">())</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;tgsp+svm&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(</span><span class="s2">&quot;oas&quot;</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">TangentSpace</span></a><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class"><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">[</span><span class="s2">&quot;MDM&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">Covariances</span></a><span class="p">(</span><span class="s2">&quot;oas&quot;</span><span class="p">),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class"><span class="n">MDM</span></a><span class="p">(</span><span class="n">metric</span><span class="o">=</span><span class="s2">&quot;riemann&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>The following lines go exactly as in the previous tutorial, where we end up
+obtaining a pandas dataframe containing the results of the evaluation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">BNCI2014_001</span></a><span class="p">(),</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class"><span class="n">Zhou2016</span></a><span class="p">()]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<span class="k">for</span> <a href="../generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016" class="sphx-glr-backref-module-moabb-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span><span class="o">.</span><span class="n">subject_list</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj</span></a>
+<a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a> <span class="o">=</span> <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class"><span class="n">LeftRightImagery</span></a><span class="p">()</span>
+<a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">evaluation</span></a> <span class="o">=</span> <a href="../generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation" class="sphx-glr-backref-module-moabb-evaluations sphx-glr-backref-type-py-class"><span class="n">WithinSessionEvaluation</span></a><span class="p">(</span>
+    <a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="o">=</span><a href="../generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery" class="sphx-glr-backref-module-moabb-paradigms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">paradigm</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">datasets</span></a><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <a href="../generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process" title="moabb.evaluations.base.BaseEvaluation.process" class="sphx-glr-backref-module-moabb-evaluations-base sphx-glr-backref-type-py-method"><span class="n">evaluation</span><span class="o">.</span><span class="n">process</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pipelines</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014-001-WithinSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession:  33%|###3      | 1/3 [00:09&lt;00:19,  9.96s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession:  67%|######6   | 2/3 [00:19&lt;00:09,  9.74s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+BNCI2014-001-WithinSession: 100%|##########| 3/3 [00:29&lt;00:00,  9.79s/it]
+BNCI2014-001-WithinSession: 100%|##########| 3/3 [00:29&lt;00:00,  9.80s/it]
+
+Zhou2016-WithinSession:   0%|          | 0/3 [00:00&lt;?, ?it/s]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession:  33%|###3      | 1/3 [00:08&lt;00:16,  8.47s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession:  67%|######6   | 2/3 [00:16&lt;00:07,  7.92s/it]No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+No hdf5_path provided, models will not be saved.
+
+Zhou2016-WithinSession: 100%|##########| 3/3 [00:23&lt;00:00,  7.63s/it]
+Zhou2016-WithinSession: 100%|##########| 3/3 [00:23&lt;00:00,  7.77s/it]
+</pre></div>
+</div>
+<p>As <cite>overwrite</cite> is set to False, the results from the previous tutorial are reused and
+only the new pipelines are evaluated. The results from “csp+lda” are not recomputed.
+The results are saved in ~/mne_data/results if the parameter <cite>hdf5_path</cite> is not set.</p>
+</section>
+<section id="plotting-results">
+<h2>Plotting Results<a class="headerlink" href="#plotting-results" title="Permalink to this heading">#</a></h2>
+<p>The following plot shows a comparison of the three classification pipelines
+for each subject of each dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;subj&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">resi</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">resi</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;subject&quot;</span><span class="p">]]</span>
+<span class="n">g</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span>
+    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;subj&quot;</span><span class="p">,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;pipeline&quot;</span><span class="p">,</span>
+    <span class="n">col</span><span class="o">=</span><span class="s2">&quot;dataset&quot;</span><span class="p">,</span>
+    <span class="n">height</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
+    <span class="n">aspect</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
+    <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;h&quot;</span><span class="p">,</span>
+    <span class="n">palette</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_001.png" srcset="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_001.png" alt="dataset = BNCI2014-001, dataset = Zhou2016" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  55.412 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  326 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/fe7c33d68ac9fa4d60c0efbc341f93f3/tutorial_3_benchmarking_multiple_pipelines.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tutorial_3_benchmarking_multiple_pipelines.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/5c05a4e8c08dd8d574a1e608b6e51c83/tutorial_3_benchmarking_multiple_pipelines.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tutorial_3_benchmarking_multiple_pipelines.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
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+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+</script></div>
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+              
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+<div class="header-article-items header-article__inner">
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+        <i class="fa-solid fa-home"></i>
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+    <li class="breadcrumb-item active" aria-current="page">Citing MOABB and related publications</li>
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+</nav>
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+      
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+  
+  
+</div>
+</div>
+              
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+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section id="citing-moabb-and-related-publications">
+<h1>Citing MOABB and related publications<a class="headerlink" href="#citing-moabb-and-related-publications" title="Permalink to this heading">#</a></h1>
+<p>If you use MOABB in your experiments, please cite this library when
+publishing a paper to increase the visibility of open science initiatives:</p>
+<ul class="simple">
+<li><p>Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Quentin, B., Schirrmeister, R. T.,Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., Roy, Y., Jayaram, V., Barachant,A., &amp; Chevallier, S.  Mother of all BCI Benchmarks (MOABB), 2023. DOI: <a class="reference external" href="https://www.doi.org/10.5281/zenodo.10034223">10.5281/zenodo.10034223</a></p></li>
+</ul>
+<p>and here is the Bibtex version:</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@software</span><span class="p">{</span><span class="nl">Aristimunha_Mother_of_all_2023</span><span class="p">,</span>
+<span class="w">  </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Aristimunha, Bruno and Carrara, Igor and Guetschel, Pierre and Sedlar, Sara and Rodrigues, Pedro and Sosulski, Jan and Narayanan, Divyesh and Bjareholt, Erik and Quentin, Barthelemy and Schirrmeister, Robin Tibor and Kalunga, Emmanuel and Darmet, Ludovic and Gregoire, Cattan and Abdul Hussain, Ali and Gatti, Ramiro and Goncharenko, Vladislav and Thielen, Jordy and Moreau, Thomas and Roy, Yannick and Jayaram, Vinay and Barachant, Alexandre and Chevallier, Sylvain}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">doi</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{10.5281/zenodo.10034223}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{{Mother of all BCI Benchmarks}}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://github.com/NeuroTechX/moabb}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">version</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1.0.0}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2023}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If you want to cite the scientific contributions of MOABB, you could use the following paper:</p>
+<ul class="simple">
+<li><p>Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Junqueira Lopes, Sébastien Velut, Salim Khazem, Thomas Moreau. <a class="reference external" href="https://cnrs.hal.science/hal-04537061/">The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark</a>, 2023. HAL: hal-04537061.</p></li>
+<li><p>Vinay Jayaram and Alexandre Barachant. MOABB: trustworthy algorithm
+benchmarking for BCIs. Journal of neural engineering 15.6 (2018):
+066011. <a class="reference external" href="https://doi.org/10.1088/1741-2552/aadea0">DOI:10.1088/1741-2552</a></p></li>
+</ul>
+<p>Here is the BibTeX entry for the above paper:</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="p">{</span><span class="nl">moabb2018</span><span class="p">,</span>
+<span class="w">  </span><span class="na">title</span><span class="p">=</span><span class="s">{MOABB: trustworthy algorithm benchmarking for BCIs}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">author</span><span class="p">=</span><span class="s">{Jayaram, Vinay and Barachant, Alexandre}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">journal</span><span class="p">=</span><span class="s">{Journal of neural engineering}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">volume</span><span class="p">=</span><span class="s">{15}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">number</span><span class="p">=</span><span class="s">{6}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">pages</span><span class="p">=</span><span class="s">{066011}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">year</span><span class="p">=</span><span class="s">{2018}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">publisher</span><span class="p">=</span><span class="s">{IOP Publishing}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If you publish a paper using MOABB, please contact us on
+<a class="reference external" href="https://app.gitter.im/#/room/#moabb_dev_community:gitter.im">gitter</a>
+or open an issue, and we will add your paper to the <a class="reference external" href="https://github.com/NeuroTechX/moabb/wiki/MOABB-bibliography">dedicated wiki
+page</a>.</p>
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diff --git a/docs/dataset_summary.html b/docs/dataset_summary.html
new file mode 100644
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+                  
+  <span class="target" id="module-moabb.datasets"><span id="data-summary"></span></span><p>A dataset handle and abstract low level access to the data. the dataset will
+takes data stored locally, in the format in which they have been downloaded,
+and will convert them into a MNE raw object. There are options to pool all the
+different recording sessions per subject or to evaluate them separately.</p>
+<p>See <a class="github reference external" href="https://github.com/NeuroTechX/moabb/wiki/Datasets-Support">NeuroTechX/moabb</a> for detail
+on datasets (electrodes, number of trials, sessions, etc.)</p>
+<section id="id1">
+<h1>Data Summary<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h1>
+<p>MOABB gather many datasets, here is list summarizing important information. Most of the
+datasets are listed here but this list not complete yet, check API for complete
+documentation.</p>
+<p>Do not hesitate to help us complete this list. It is also possible to add new datasets,
+there is a tutorial explaining how to do so, and we welcome warmly any new contributions!</p>
+<p>See also <a class="reference external" href="https://github.com/NeuroTechX/moabb/wiki/Datasets-Support">Datasets-Support</a> for supplementary
+detail on datasets (class name, size, licence, etc.)</p>
+</section>
+<section id="motor-imagery">
+<h1>Motor Imagery<a class="headerlink" href="#motor-imagery" title="Permalink to this heading">#</a></h1>
+<table class="sortable table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Dataset</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials</p></th>
+<th class="head"><p>Trial length</p></th>
+<th class="head"><p>Freq</p></th>
+<th class="head"><p>#Session</p></th>
+<th class="head"><p>#Runs</p></th>
+<th class="head"><p>Total_trials</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">AlexMI</span></code></a></p></td>
+<td><p>8</p></td>
+<td><p>16</p></td>
+<td><p>3</p></td>
+<td><p>20</p></td>
+<td><p>3s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+<td><p>1</p></td>
+<td><p>480</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></a></p></td>
+<td><p>9</p></td>
+<td><p>22</p></td>
+<td><p>4</p></td>
+<td><p>144</p></td>
+<td><p>4s</p></td>
+<td><p>250Hz</p></td>
+<td><p>2</p></td>
+<td><p>6</p></td>
+<td><p>62208</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_002.html#moabb.datasets.BNCI2014_002" title="moabb.datasets.BNCI2014_002"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_002</span></code></a></p></td>
+<td><p>14</p></td>
+<td><p>15</p></td>
+<td><p>2</p></td>
+<td><p>80</p></td>
+<td><p>5s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+<td><p>8</p></td>
+<td><p>17920</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_004</span></code></a></p></td>
+<td><p>9</p></td>
+<td><p>3</p></td>
+<td><p>2</p></td>
+<td><p>360</p></td>
+<td><p>4.5s</p></td>
+<td><p>250Hz</p></td>
+<td><p>5</p></td>
+<td><p>1</p></td>
+<td><p>32400</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_001.html#moabb.datasets.BNCI2015_001" title="moabb.datasets.BNCI2015_001"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2015_001</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>13</p></td>
+<td><p>2</p></td>
+<td><p>200</p></td>
+<td><p>5s</p></td>
+<td><p>512Hz</p></td>
+<td><p>3</p></td>
+<td><p>1</p></td>
+<td><p>14400</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_004.html#moabb.datasets.BNCI2015_004" title="moabb.datasets.BNCI2015_004"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2015_004</span></code></a></p></td>
+<td><p>9</p></td>
+<td><p>30</p></td>
+<td><p>5</p></td>
+<td><p>80</p></td>
+<td><p>7s</p></td>
+<td><p>256Hz</p></td>
+<td><p>2</p></td>
+<td><p>1</p></td>
+<td><p>7200</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017" title="moabb.datasets.Cho2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cho2017</span></code></a></p></td>
+<td><p>52</p></td>
+<td><p>64</p></td>
+<td><p>2</p></td>
+<td><p>100</p></td>
+<td><p>3s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+<td><p>1</p></td>
+<td><p>9800</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_MI.html#moabb.datasets.Lee2019_MI" title="moabb.datasets.Lee2019_MI"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lee2019_MI</span></code></a></p></td>
+<td><p>54</p></td>
+<td><p>62</p></td>
+<td><p>2</p></td>
+<td><p>100</p></td>
+<td><p>4s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>2</p></td>
+<td><p>1</p></td>
+<td><p>11000</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.GrosseWentrup2009.html#moabb.datasets.GrosseWentrup2009" title="moabb.datasets.GrosseWentrup2009"><code class="xref py py-class docutils literal notranslate"><span class="pre">GrosseWentrup2009</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>128</p></td>
+<td><p>2</p></td>
+<td><p>150</p></td>
+<td><p>7s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+<td><p>1</p></td>
+<td><p>3000</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017" title="moabb.datasets.Schirrmeister2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a></p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+<td><p>2</p></td>
+<td><p>13440</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Ofner2017.html#moabb.datasets.Ofner2017" title="moabb.datasets.Ofner2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ofner2017</span></code></a></p></td>
+<td><p>15</p></td>
+<td><p>61</p></td>
+<td><p>7</p></td>
+<td><p>60</p></td>
+<td><p>3s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+<td><p>10</p></td>
+<td><p>63000</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a></p></td>
+<td><p>109</p></td>
+<td><p>64</p></td>
+<td><p>4</p></td>
+<td><p>23</p></td>
+<td><p>3s</p></td>
+<td><p>160Hz</p></td>
+<td><p>1</p></td>
+<td><p>1</p></td>
+<td><p>69760</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Shin2017A.html#moabb.datasets.Shin2017A" title="moabb.datasets.Shin2017A"><code class="xref py py-class docutils literal notranslate"><span class="pre">Shin2017A</span></code></a></p></td>
+<td><p>29</p></td>
+<td><p>30</p></td>
+<td><p>2</p></td>
+<td><p>30</p></td>
+<td><p>10s</p></td>
+<td><p>200Hz</p></td>
+<td><p>3</p></td>
+<td><p>1</p></td>
+<td><p>5220</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Shin2017B.html#moabb.datasets.Shin2017B" title="moabb.datasets.Shin2017B"><code class="xref py py-class docutils literal notranslate"><span class="pre">Shin2017B</span></code></a></p></td>
+<td><p>29</p></td>
+<td><p>30</p></td>
+<td><p>2</p></td>
+<td><p>30</p></td>
+<td><p>10s</p></td>
+<td><p>200Hz</p></td>
+<td><p>3</p></td>
+<td><p>1</p></td>
+<td><p>5220</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014" title="moabb.datasets.Weibo2014"><code class="xref py py-class docutils literal notranslate"><span class="pre">Weibo2014</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>60</p></td>
+<td><p>7</p></td>
+<td><p>80</p></td>
+<td><p>4s</p></td>
+<td><p>200Hz</p></td>
+<td><p>1</p></td>
+<td><p>1</p></td>
+<td><p>5600</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Zhou2016</span></code></a></p></td>
+<td><p>4</p></td>
+<td><p>14</p></td>
+<td><p>3</p></td>
+<td><p>160</p></td>
+<td><p>5s</p></td>
+<td><p>250Hz</p></td>
+<td><p>3</p></td>
+<td><p>2</p></td>
+<td><p>11496</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="p300-erp">
+<h1>P300/ERP<a class="headerlink" href="#p300-erp" title="Permalink to this heading">#</a></h1>
+<table class="sortable table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Dataset</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_008.html#moabb.datasets.BNCI2014_008" title="moabb.datasets.BNCI2014_008"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_008</span></code></a></p></td>
+<td><p>8</p></td>
+<td><p>8</p></td>
+<td><p>3500 NT / 700 T</p></td>
+<td><p>1s</p></td>
+<td><p>256Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_009</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>16</p></td>
+<td><p>1440 NT / 288 T</p></td>
+<td><p>0.8s</p></td>
+<td><p>256Hz</p></td>
+<td><p>3</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_003.html#moabb.datasets.BNCI2015_003" title="moabb.datasets.BNCI2015_003"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2015_003</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>8</p></td>
+<td><p>1500 NT / 300 T</p></td>
+<td><p>0.8s</p></td>
+<td><p>256Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2012.html#moabb.datasets.BI2012" title="moabb.datasets.BI2012"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2012</span></code></a></p></td>
+<td><p>25</p></td>
+<td><p>16</p></td>
+<td><p>640 NT / 128 T</p></td>
+<td><p>1s</p></td>
+<td><p>128Hz</p></td>
+<td><p>2</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2013a.html#moabb.datasets.BI2013a" title="moabb.datasets.BI2013a"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2013a</span></code></a></p></td>
+<td><p>24</p></td>
+<td><p>16</p></td>
+<td><p>3200 NT / 640 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>8 for subjects 1-7 else 1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a" title="moabb.datasets.BI2014a"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014a</span></code></a></p></td>
+<td><p>64</p></td>
+<td><p>16</p></td>
+<td><p>990 NT / 198 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>up to 3</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2014b.html#moabb.datasets.BI2014b" title="moabb.datasets.BI2014b"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014b</span></code></a></p></td>
+<td><p>38</p></td>
+<td><p>32</p></td>
+<td><p>200 NT / 40 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>3</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2015a.html#moabb.datasets.BI2015a" title="moabb.datasets.BI2015a"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015a</span></code></a></p></td>
+<td><p>43</p></td>
+<td><p>32</p></td>
+<td><p>4131 NT / 825 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>3</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2015b.html#moabb.datasets.BI2015b" title="moabb.datasets.BI2015b"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015b</span></code></a></p></td>
+<td><p>44</p></td>
+<td><p>32</p></td>
+<td><p>2160 NT / 480 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cattan2019_VR</span></code></a></p></td>
+<td><p>21</p></td>
+<td><p>16</p></td>
+<td><p>600 NT / 120 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>2</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Huebner2017.html#moabb.datasets.Huebner2017" title="moabb.datasets.Huebner2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Huebner2017</span></code></a></p></td>
+<td><p>13</p></td>
+<td><p>31</p></td>
+<td><p>364 NT / 112 T</p></td>
+<td><p>0.9s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>3</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Huebner2018.html#moabb.datasets.Huebner2018" title="moabb.datasets.Huebner2018"><code class="xref py py-class docutils literal notranslate"><span class="pre">Huebner2018</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>31</p></td>
+<td><p>364 NT / 112 T</p></td>
+<td><p>0.9s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>3</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019" title="moabb.datasets.Sosulski2019"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sosulski2019</span></code></a></p></td>
+<td><p>13</p></td>
+<td><p>31</p></td>
+<td><p>7500 NT / 1500 T</p></td>
+<td><p>1.2s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.EPFLP300.html#moabb.datasets.EPFLP300" title="moabb.datasets.EPFLP300"><code class="xref py py-class docutils literal notranslate"><span class="pre">EPFLP300</span></code></a></p></td>
+<td><p>8</p></td>
+<td><p>32</p></td>
+<td><p>2753 NT / 551 T</p></td>
+<td><p>1s</p></td>
+<td><p>2048Hz</p></td>
+<td><p>4</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_ERP.html#moabb.datasets.Lee2019_ERP" title="moabb.datasets.Lee2019_ERP"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lee2019_ERP</span></code></a></p></td>
+<td><p>54</p></td>
+<td><p>62</p></td>
+<td><p>6900 NT / 1380 T</p></td>
+<td><p>1s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>2</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="ssvep">
+<h1>SSVEP<a class="headerlink" href="#ssvep" title="Permalink to this heading">#</a></h1>
+<table class="sortable table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Dataset</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_SSVEP.html#moabb.datasets.Lee2019_SSVEP" title="moabb.datasets.Lee2019_SSVEP"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lee2019_SSVEP</span></code></a></p></td>
+<td><p>54</p></td>
+<td><p>62</p></td>
+<td><p>4</p></td>
+<td><p>50</p></td>
+<td><p>4s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>2</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kalunga2016</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>8</p></td>
+<td><p>4</p></td>
+<td><p>16</p></td>
+<td><p>2s</p></td>
+<td><p>256Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM1.html#moabb.datasets.MAMEM1" title="moabb.datasets.MAMEM1"><code class="xref py py-class docutils literal notranslate"><span class="pre">MAMEM1</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>256</p></td>
+<td><p>5</p></td>
+<td><p>12-15</p></td>
+<td><p>3s</p></td>
+<td><p>250Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM2.html#moabb.datasets.MAMEM2" title="moabb.datasets.MAMEM2"><code class="xref py py-class docutils literal notranslate"><span class="pre">MAMEM2</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>256</p></td>
+<td><p>5</p></td>
+<td><p>20-30</p></td>
+<td><p>3s</p></td>
+<td><p>250Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3"><code class="xref py py-class docutils literal notranslate"><span class="pre">MAMEM3</span></code></a></p></td>
+<td><p>10</p></td>
+<td><p>14</p></td>
+<td><p>4</p></td>
+<td><p>20-30</p></td>
+<td><p>3s</p></td>
+<td><p>128Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Nakanishi2015.html#moabb.datasets.Nakanishi2015" title="moabb.datasets.Nakanishi2015"><code class="xref py py-class docutils literal notranslate"><span class="pre">Nakanishi2015</span></code></a></p></td>
+<td><p>9</p></td>
+<td><p>8</p></td>
+<td><p>12</p></td>
+<td><p>15</p></td>
+<td><p>4.15s</p></td>
+<td><p>256Hz</p></td>
+<td><p>1</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Wang2016.html#moabb.datasets.Wang2016" title="moabb.datasets.Wang2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Wang2016</span></code></a></p></td>
+<td><p>34</p></td>
+<td><p>62</p></td>
+<td><p>40</p></td>
+<td><p>6</p></td>
+<td><p>5s</p></td>
+<td><p>250Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="c-vep">
+<h1>c-VEP<a class="headerlink" href="#c-vep" title="Permalink to this heading">#</a></h1>
+<p>Include neuro experiments where the participant is presented with psuedo-random noise-codes,
+such as m-sequences, Gold codes, or any arbitrary “pseudo-random” code. Specifically, the
+difference with SSVEP is that SSVEP presents periodic stimuli, while c-VEP presents
+non-periodic stimuli. For a review of c-VEP BCI, see:</p>
+<p>Martínez-Cagigal, V., Thielen, J., Santamaria-Vazquez, E., Pérez-Velasco, S., Desain, P.,&amp;
+Hornero, R. (2021). Brain–computer interfaces based on code-modulated visual evoked
+potentials (c-VEP): A literature review. Journal of Neural Engineering, 18(6), 061002.
+DOI: <a class="reference external" href="https://doi.org/10.1088/1741-2552/ac38cf">https://doi.org/10.1088/1741-2552/ac38cf</a></p>
+<table class="sortable table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Dataset</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Sessions</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>#Trial classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>#Epochs classes</p></th>
+<th class="head"><p>#Epochs / class</p></th>
+<th class="head"><p>Codes</p></th>
+<th class="head"><p>Presentation rate</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Thielen2015.html#moabb.datasets.Thielen2015" title="moabb.datasets.Thielen2015"><code class="xref py py-class docutils literal notranslate"><span class="pre">Thielen2015</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>2048Hz</p></td>
+<td><p>64</p></td>
+<td><p>4.2s</p></td>
+<td><p>36</p></td>
+<td><p>3</p></td>
+<td><p>2</p></td>
+<td><p>27216 NT / 27216 T</p></td>
+<td><p>Gold codes</p></td>
+<td><p>120Hz</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Thielen2021.html#moabb.datasets.Thielen2021" title="moabb.datasets.Thielen2021"><code class="xref py py-class docutils literal notranslate"><span class="pre">Thielen2021</span></code></a></p></td>
+<td><p>30</p></td>
+<td><p>1</p></td>
+<td><p>512Hz</p></td>
+<td><p>8</p></td>
+<td><p>31.5s</p></td>
+<td><p>20</p></td>
+<td><p>5</p></td>
+<td><p>2</p></td>
+<td><p>18900 NT / 18900 T</p></td>
+<td><p>Gold codes</p></td>
+<td><p>60Hz</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosCVEP100.html#moabb.datasets.CastillosCVEP100" title="moabb.datasets.CastillosCVEP100"><code class="xref py py-class docutils literal notranslate"><span class="pre">CastillosCVEP100</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>3525 NT / 3495 T</p></td>
+<td><p>m-sequence</p></td>
+<td><p>60Hz</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosCVEP40.html#moabb.datasets.CastillosCVEP40" title="moabb.datasets.CastillosCVEP40"><code class="xref py py-class docutils literal notranslate"><span class="pre">CastillosCVEP40</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>3525 NT / 3495 T</p></td>
+<td><p>m-sequence</p></td>
+<td><p>60Hz</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosBurstVEP40.html#moabb.datasets.CastillosBurstVEP40" title="moabb.datasets.CastillosBurstVEP40"><code class="xref py py-class docutils literal notranslate"><span class="pre">CastillosBurstVEP40</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>5820 NT / 1200 T</p></td>
+<td><p>Burst-CVEP</p></td>
+<td><p>60Hz</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosBurstVEP100.html#moabb.datasets.CastillosBurstVEP100" title="moabb.datasets.CastillosBurstVEP100"><code class="xref py py-class docutils literal notranslate"><span class="pre">CastillosBurstVEP100</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>5820 NT / 1200 T</p></td>
+<td><p>Burst-CVEP</p></td>
+<td><p>60Hz</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="resting-states">
+<h1>Resting States<a class="headerlink" href="#resting-states" title="Permalink to this heading">#</a></h1>
+<p>Include neuro experiments where the participant is not actively doing something.
+For example, recoding the EEG of a subject while s/he is having the eye closed or opened
+is a resting state experiment.</p>
+<table class="sortable table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Dataset</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Blocks / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cattan2019_PHMD</span></code></a></p></td>
+<td><p>12</p></td>
+<td><p>16</p></td>
+<td><p>2</p></td>
+<td><p>10</p></td>
+<td><p>60s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="module-moabb.datasets.compound_dataset">
+<span id="compound-datasets"></span><h1>Compound Datasets<a class="headerlink" href="#module-moabb.datasets.compound_dataset" title="Permalink to this heading">#</a></h1>
+<p>Compound Datasets are datasets compounded with subjects from other datasets.
+It is useful for merging different datasets (including other Compound Datasets),
+select a sample of subject inside a dataset (e.g. subject with high/low performance).</p>
+<table class="sortable table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Dataset</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Original datasets</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014a_Il.html#moabb.datasets.compound_dataset.BI2014a_Il" title="moabb.datasets.compound_dataset.BI2014a_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014a_Il</span></code></a></p></td>
+<td><p>17</p></td>
+<td><p>BI2014a</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014b_Il.html#moabb.datasets.compound_dataset.BI2014b_Il" title="moabb.datasets.compound_dataset.BI2014b_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014b_Il</span></code></a></p></td>
+<td><p>11</p></td>
+<td><p>BI2014b</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015a_Il.html#moabb.datasets.compound_dataset.BI2015a_Il" title="moabb.datasets.compound_dataset.BI2015a_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015a_Il</span></code></a></p></td>
+<td><p>2</p></td>
+<td><p>BI2015a</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015b_Il.html#moabb.datasets.compound_dataset.BI2015b_Il" title="moabb.datasets.compound_dataset.BI2015b_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015b_Il</span></code></a></p></td>
+<td><p>25</p></td>
+<td><p>BI2015b</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html#moabb.datasets.compound_dataset.Cattan2019_VR_Il" title="moabb.datasets.compound_dataset.Cattan2019_VR_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cattan2019_VR_Il</span></code></a></p></td>
+<td><p>4</p></td>
+<td><p>Cattan2019_VR</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI_Il.html#moabb.datasets.compound_dataset.BI_Il" title="moabb.datasets.compound_dataset.BI_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI_Il</span></code></a></p></td>
+<td><p>59</p></td>
+<td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014a_Il.html#moabb.datasets.compound_dataset.BI2014a_Il" title="moabb.datasets.compound_dataset.BI2014a_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014a_Il</span></code></a> <a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014b_Il.html#moabb.datasets.compound_dataset.BI2014b_Il" title="moabb.datasets.compound_dataset.BI2014b_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014b_Il</span></code></a> <a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015a_Il.html#moabb.datasets.compound_dataset.BI2015a_Il" title="moabb.datasets.compound_dataset.BI2015a_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015a_Il</span></code></a> <a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015b_Il.html#moabb.datasets.compound_dataset.BI2015b_Il" title="moabb.datasets.compound_dataset.BI2015b_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015b_Il</span></code></a> <a class="reference internal" href="generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html#moabb.datasets.compound_dataset.Cattan2019_VR_Il" title="moabb.datasets.compound_dataset.Cattan2019_VR_Il"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cattan2019_VR_Il</span></code></a></p></td>
+</tr>
+</tbody>
+</table>
+<section id="submit-a-new-dataset">
+<h2>Submit a new dataset<a class="headerlink" href="#submit-a-new-dataset" title="Permalink to this heading">#</a></h2>
+<p>you can submit a new dataset by mentioning it to this
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/1">issue</a>. The datasets
+currently on our radar can be seen <a class="reference external" href="https://github.com/NeuroTechX/moabb/wiki/Datasets-Support">here</a>,
+but we are open to any suggestion.</p>
+<p>If you want to actively contribute to inclusion of one new dataset, you can follow also this tutorial
+<a class="reference external" href="https://neurotechx.github.io/moabb/auto_tutorials/tutorial_4_adding_a_dataset.html">tutorial</a>.</p>
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+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#p300-erp">P300/ERP</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#ssvep">SSVEP</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#c-vep">c-VEP</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#resting-states">Resting States</a></li>
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+  <section id="module-moabb.datasets">
+<span id="datasets"></span><h1>Datasets<a class="headerlink" href="#module-moabb.datasets" title="Permalink to this heading">#</a></h1>
+<p>A dataset handle and abstract low level access to the data. the dataset will
+takes data stored locally, in the format in which they have been downloaded,
+and will convert them into a MNE raw object. There are options to pool all the
+different recording sessions per subject or to evaluate them separately.</p>
+<p>See <a class="github reference external" href="https://github.com/NeuroTechX/moabb/wiki/Datasets-Support">NeuroTechX/moabb</a> for detail
+on datasets (electrodes, number of trials, sessions, etc.)</p>
+<section id="motor-imagery-datasets">
+<h2>Motor Imagery Datasets<a class="headerlink" href="#motor-imagery-datasets" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AlexMI</span></code></a>()</p></td>
+<td><p>Alex Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></a>()</p></td>
+<td><p>BNCI 2014-001 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_002.html#moabb.datasets.BNCI2014_002" title="moabb.datasets.BNCI2014_002"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_002</span></code></a>()</p></td>
+<td><p>BNCI 2014-002 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_004</span></code></a>()</p></td>
+<td><p>BNCI 2014-004 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_001.html#moabb.datasets.BNCI2015_001" title="moabb.datasets.BNCI2015_001"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2015_001</span></code></a>()</p></td>
+<td><p>BNCI 2015-001 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_004.html#moabb.datasets.BNCI2015_004" title="moabb.datasets.BNCI2015_004"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2015_004</span></code></a>()</p></td>
+<td><p>BNCI 2015-004 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017" title="moabb.datasets.Cho2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cho2017</span></code></a>()</p></td>
+<td><p>Motor Imagery dataset from Cho et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_MI.html#moabb.datasets.Lee2019_MI" title="moabb.datasets.Lee2019_MI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lee2019_MI</span></code></a>([train_run, test_run, ...])</p></td>
+<td><p>BMI/OpenBMI dataset for MI.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.GrosseWentrup2009.html#moabb.datasets.GrosseWentrup2009" title="moabb.datasets.GrosseWentrup2009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GrosseWentrup2009</span></code></a>()</p></td>
+<td><p>Munich Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Ofner2017.html#moabb.datasets.Ofner2017" title="moabb.datasets.Ofner2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ofner2017</span></code></a>([imagined, executed])</p></td>
+<td><p>Motor Imagery ataset from Ofner et al 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a>([imagined, executed])</p></td>
+<td><p>Physionet Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017" title="moabb.datasets.Schirrmeister2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a>()</p></td>
+<td><p>High-gamma dataset described in Schirrmeister et al. 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Shin2017A.html#moabb.datasets.Shin2017A" title="moabb.datasets.Shin2017A"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Shin2017A</span></code></a>([accept])</p></td>
+<td><p>Motor Imagey Dataset from Shin et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Shin2017B.html#moabb.datasets.Shin2017B" title="moabb.datasets.Shin2017B"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Shin2017B</span></code></a>([accept])</p></td>
+<td><p>Mental Arithmetic Dataset from Shin et al 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014" title="moabb.datasets.Weibo2014"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Weibo2014</span></code></a>()</p></td>
+<td><p>Motor Imagery dataset from Weibo et al 2014.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Zhou2016</span></code></a>()</p></td>
+<td><p>Motor Imagery dataset from Zhou et al 2016.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="erp-datasets">
+<h2>ERP Datasets<a class="headerlink" href="#erp-datasets" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2012.html#moabb.datasets.BI2012" title="moabb.datasets.BI2012"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2012</span></code></a>([Training, Online])</p></td>
+<td><p>P300 dataset BI2012 from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2013a.html#moabb.datasets.BI2013a" title="moabb.datasets.BI2013a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2013a</span></code></a>([NonAdaptive, Adaptive, Training, ...])</p></td>
+<td><p>P300 dataset BI2013a from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a" title="moabb.datasets.BI2014a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014a</span></code></a>()</p></td>
+<td><p>P300 dataset BI2014a from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2014b.html#moabb.datasets.BI2014b" title="moabb.datasets.BI2014b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014b</span></code></a>()</p></td>
+<td><p>P300 dataset BI2014b from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2015a.html#moabb.datasets.BI2015a" title="moabb.datasets.BI2015a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015a</span></code></a>()</p></td>
+<td><p>P300 dataset BI2015a from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BI2015b.html#moabb.datasets.BI2015b" title="moabb.datasets.BI2015b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015b</span></code></a>()</p></td>
+<td><p>P300 dataset BI2015b from a &quot;Brain Invaders&quot; experiment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_VR</span></code></a>([virtual_reality, screen_display])</p></td>
+<td><p>Dataset of an EEG-based BCI experiment in Virtual Reality using P300.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_008.html#moabb.datasets.BNCI2014_008" title="moabb.datasets.BNCI2014_008"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_008</span></code></a>()</p></td>
+<td><p>BNCI 2014-008 P300 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014_009</span></code></a>()</p></td>
+<td><p>BNCI 2014-009 P300 dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_003.html#moabb.datasets.BNCI2015_003" title="moabb.datasets.BNCI2015_003"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2015_003</span></code></a>()</p></td>
+<td><p>BNCI 2015-003 P300 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.DemonsP300.html#moabb.datasets.DemonsP300" title="moabb.datasets.DemonsP300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DemonsP300</span></code></a>()</p></td>
+<td><p>Visual P300 dataset recorded in Virtual Reality (VR) game Raccoons versus Demons.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.EPFLP300.html#moabb.datasets.EPFLP300" title="moabb.datasets.EPFLP300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EPFLP300</span></code></a>()</p></td>
+<td><p>P300 dataset from Hoffmann et al 2008.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Huebner2017.html#moabb.datasets.Huebner2017" title="moabb.datasets.Huebner2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Huebner2017</span></code></a>([interval, raw_slice_offset, ...])</p></td>
+<td><p>Learning from label proportions for a visual matrix speller (ERP) dataset from Hübner et al 2017 <a class="reference internal" href="generated/moabb.datasets.Huebner2017.html#r0a211c89d39d-1" id="id1"><span>[R0a211c89d39d-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Huebner2018.html#moabb.datasets.Huebner2018" title="moabb.datasets.Huebner2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Huebner2018</span></code></a>([interval, raw_slice_offset, ...])</p></td>
+<td><p>Mixture of LLP and EM for a visual matrix speller (ERP) dataset from Hübner et al 2018 <a class="reference internal" href="generated/moabb.datasets.Huebner2018.html#r8f30fc0d0ace-1" id="id2"><span>[R8f30fc0d0ace-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_ERP.html#moabb.datasets.Lee2019_ERP" title="moabb.datasets.Lee2019_ERP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lee2019_ERP</span></code></a>([train_run, test_run, ...])</p></td>
+<td><p>BMI/OpenBMI dataset for P300.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019" title="moabb.datasets.Sosulski2019"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sosulski2019</span></code></a>([use_soas_as_sessions, ...])</p></td>
+<td><p>P300 dataset from initial spot study.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="ssvep-datasets">
+<h2>SSVEP Datasets<a class="headerlink" href="#ssvep-datasets" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Kalunga2016</span></code></a>()</p></td>
+<td><p>SSVEP Exo dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Nakanishi2015.html#moabb.datasets.Nakanishi2015" title="moabb.datasets.Nakanishi2015"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Nakanishi2015</span></code></a>()</p></td>
+<td><p>SSVEP Nakanishi 2015 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Wang2016.html#moabb.datasets.Wang2016" title="moabb.datasets.Wang2016"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Wang2016</span></code></a>()</p></td>
+<td><p>SSVEP Wang 2016 dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM1.html#moabb.datasets.MAMEM1" title="moabb.datasets.MAMEM1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAMEM1</span></code></a>()</p></td>
+<td><p>SSVEP MAMEM 1 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM2.html#moabb.datasets.MAMEM2" title="moabb.datasets.MAMEM2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAMEM2</span></code></a>()</p></td>
+<td><p>SSVEP MAMEM 2 dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAMEM3</span></code></a>()</p></td>
+<td><p>SSVEP MAMEM 3 dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_SSVEP.html#moabb.datasets.Lee2019_SSVEP" title="moabb.datasets.Lee2019_SSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lee2019_SSVEP</span></code></a>([train_run, test_run, ...])</p></td>
+<td><p>BMI/OpenBMI dataset for SSVEP.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="c-vep-datasets">
+<h2>c-VEP Datasets<a class="headerlink" href="#c-vep-datasets" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Thielen2015.html#moabb.datasets.Thielen2015" title="moabb.datasets.Thielen2015"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Thielen2015</span></code></a>()</p></td>
+<td><p>c-VEP dataset from Thielen et al. (2015).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.Thielen2021.html#moabb.datasets.Thielen2021" title="moabb.datasets.Thielen2021"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Thielen2021</span></code></a>()</p></td>
+<td><p>c-VEP dataset from Thielen et al. (2021).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosBurstVEP40.html#moabb.datasets.CastillosBurstVEP40" title="moabb.datasets.CastillosBurstVEP40"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosBurstVEP40</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosBurstVEP100.html#moabb.datasets.CastillosBurstVEP100" title="moabb.datasets.CastillosBurstVEP100"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosBurstVEP100</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosCVEP40.html#moabb.datasets.CastillosCVEP40" title="moabb.datasets.CastillosCVEP40"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosCVEP40</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.CastillosCVEP100.html#moabb.datasets.CastillosCVEP100" title="moabb.datasets.CastillosCVEP100"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastillosCVEP100</span></code></a>()</p></td>
+<td><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023).</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="resting-state-datasets">
+<h2>Resting State Datasets<a class="headerlink" href="#resting-state-datasets" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD" title="moabb.datasets.Cattan2019_PHMD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_PHMD</span></code></a>()</p></td>
+<td><p>Passive Head Mounted Display with Music Listening dataset.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="base-utils">
+<h2>Base &amp; Utils<a class="headerlink" href="#base-utils" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseDataset</span></code></a>(subjects, ...[, doi, ...])</p></td>
+<td><p>Abstract Moabb BaseDataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.CacheConfig</span></code></a>([save_raw, save_epochs, ...])</p></td>
+<td><p>Configuration for caching of datasets.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.fake.FakeDataset.html#moabb.datasets.fake.FakeDataset" title="moabb.datasets.fake.FakeDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fake.FakeDataset</span></code></a>([event_list, n_sessions, ...])</p></td>
+<td><p>Fake Dataset for test purpose.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.fake.FakeVirtualRealityDataset.html#moabb.datasets.fake.FakeVirtualRealityDataset" title="moabb.datasets.fake.FakeVirtualRealityDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fake.FakeVirtualRealityDataset</span></code></a>([seed])</p></td>
+<td><p>Fake Cattan2019_VR dataset for test purpose.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.data_path.html#moabb.datasets.download.data_path" title="moabb.datasets.download.data_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.data_path</span></code></a>(url, sign[, path, ...])</p></td>
+<td><p>Get path to local copy of given dataset URL.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.download.data_dl.html#moabb.datasets.download.data_dl" title="moabb.datasets.download.data_dl"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.data_dl</span></code></a>(url, sign[, path, ...])</p></td>
+<td><p>Download file from url to specified path.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_issue_request.html#moabb.datasets.download.fs_issue_request" title="moabb.datasets.download.fs_issue_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_issue_request</span></code></a>(method, url, headers)</p></td>
+<td><p>Wrapper for HTTP request.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_list.html#moabb.datasets.download.fs_get_file_list" title="moabb.datasets.download.fs_get_file_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_list</span></code></a>(article_id[, version])</p></td>
+<td><p>List all the files associated with a given article.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_hash.html#moabb.datasets.download.fs_get_file_hash" title="moabb.datasets.download.fs_get_file_hash"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_hash</span></code></a>(filelist)</p></td>
+<td><p>Returns a dict associating figshare file id to MD5 hash.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_id.html#moabb.datasets.download.fs_get_file_id" title="moabb.datasets.download.fs_get_file_id"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_id</span></code></a>(filelist)</p></td>
+<td><p>Returns a dict associating filename to figshare file id.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.download.fs_get_file_name.html#moabb.datasets.download.fs_get_file_name" title="moabb.datasets.download.fs_get_file_name"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download.fs_get_file_name</span></code></a>(filelist)</p></td>
+<td><p>Returns a dict associating figshare file id to filename.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.utils.dataset_search.html#moabb.datasets.utils.dataset_search" title="moabb.datasets.utils.dataset_search"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.dataset_search</span></code></a>([paradigm, ...])</p></td>
+<td><p>Returns a list of datasets that match a given criteria.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.utils.find_intersecting_channels.html#moabb.datasets.utils.find_intersecting_channels" title="moabb.datasets.utils.find_intersecting_channels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.find_intersecting_channels</span></code></a>(datasets[, ...])</p></td>
+<td><p>Given a list of dataset instances return a list of channels shared by all datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="module-moabb.datasets.compound_dataset">
+<span id="compound-datasets"></span><h1>Compound Datasets<a class="headerlink" href="#module-moabb.datasets.compound_dataset" title="Permalink to this heading">#</a></h1>
+<section id="id3">
+<h2>ERP Datasets<a class="headerlink" href="#id3" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014a_Il.html#moabb.datasets.compound_dataset.BI2014a_Il" title="moabb.datasets.compound_dataset.BI2014a_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014a_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2014a with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2014b_Il.html#moabb.datasets.compound_dataset.BI2014b_Il" title="moabb.datasets.compound_dataset.BI2014b_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2014b_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2014b with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015a_Il.html#moabb.datasets.compound_dataset.BI2015a_Il" title="moabb.datasets.compound_dataset.BI2015a_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015a_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2015a with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI2015b_Il.html#moabb.datasets.compound_dataset.BI2015b_Il" title="moabb.datasets.compound_dataset.BI2015b_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI2015b_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from BI2015b with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html#moabb.datasets.compound_dataset.Cattan2019_VR_Il" title="moabb.datasets.compound_dataset.Cattan2019_VR_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_VR_Il</span></code></a>()</p></td>
+<td><p>A selection of subject from Cattan2019_VR with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.datasets.compound_dataset.BI_Il.html#moabb.datasets.compound_dataset.BI_Il" title="moabb.datasets.compound_dataset.BI_Il"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BI_Il</span></code></a>()</p></td>
+<td><p>Subjects from braininvaders datasets with AUC &lt; 0.7 with pipeline: ERPCovariances(estimator=&quot;lwf&quot;), MDM(metric=&quot;riemann&quot;)</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+
+
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+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
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+<span id="evaluations"></span><h1>Evaluations<a class="headerlink" href="#module-moabb.evaluations" title="Permalink to this heading">#</a></h1>
+<p>An evaluation defines how we go from trials per subject and session to a
+generalization statistic (AUC score, f-score, accuracy, etc) – it can be
+either within-recording-session accuracy, across-session within-subject
+accuracy, across-subject accuracy, or other transfer learning settings.</p>
+<section id="id1">
+<h2>Evaluations<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WithinSessionEvaluation</span></code></a>([n_perms, data_size])</p></td>
+<td><p>Performance evaluation within session (k-fold cross-validation)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossSessionEvaluation</span></code></a>(paradigm[, datasets, ...])</p></td>
+<td><p>Cross-session performance evaluation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation" title="moabb.evaluations.CrossSubjectEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossSubjectEvaluation</span></code></a>(paradigm[, datasets, ...])</p></td>
+<td><p>Cross-subject evaluation performance.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="base-utils">
+<h2>Base &amp; Utils<a class="headerlink" href="#base-utils" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation" title="moabb.evaluations.base.BaseEvaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseEvaluation</span></code></a>(paradigm[, datasets, ...])</p></td>
+<td><p>Base class that defines necessary operations for an evaluation.</p></td>
+</tr>
+</tbody>
+</table>
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\ No newline at end of file
diff --git a/docs/generated/moabb.analysis.meta_analysis.collapse_session_scores.html b/docs/generated/moabb.analysis.meta_analysis.collapse_session_scores.html
new file mode 100644
index 00000000..cbd91f13
--- /dev/null
+++ b/docs/generated/moabb.analysis.meta_analysis.collapse_session_scores.html
@@ -0,0 +1,756 @@
+
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+  <section id="moabb-analysis-meta-analysis-collapse-session-scores">
+<h1>moabb.analysis.meta_analysis.collapse_session_scores<a class="headerlink" href="#moabb-analysis-meta-analysis-collapse-session-scores" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.meta_analysis.collapse_session_scores">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.meta_analysis.</span></span><span class="sig-name descname"><span class="pre">collapse_session_scores</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/meta_analysis.py#L14-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.meta_analysis.collapse_session_scores" title="Permalink to this definition">#</a></dt>
+<dd><p>Prepare results dataframe for computing statistics.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>df</strong> (<em>DataFrame</em>) – results from evaluation</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>df</strong> – Aggregated results, samples are index, columns are pipelines,
+and values are scores</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>DataFrame</p>
+</dd>
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+  <section id="moabb-analysis-meta-analysis-combine-effects">
+<h1>moabb.analysis.meta_analysis.combine_effects<a class="headerlink" href="#moabb-analysis-meta-analysis-combine-effects" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.meta_analysis.combine_effects">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.meta_analysis.</span></span><span class="sig-name descname"><span class="pre">combine_effects</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">effects</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsubs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/meta_analysis.py#L249-L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.meta_analysis.combine_effects" title="Permalink to this definition">#</a></dt>
+<dd><p>Combine effects for meta-analysis statistics.</p>
+<p>Function that takes effects from each experiments and number of subjects to
+return meta-analysis effect</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>effects</strong> (<em>DataFrame</em>) – effects for 2 pipelines computed on different datasets</p></li>
+<li><p><strong>nsubs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – average number of subject per datasets</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>effect</strong> – Estimatation of the combined effects</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.analysis.meta_analysis.combine_effects"><code class="docutils literal notranslate"><span class="pre">combine_effects()</span></code></a></li>
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+  <section id="moabb-analysis-meta-analysis-combine-pvalues">
+<h1>moabb.analysis.meta_analysis.combine_pvalues<a class="headerlink" href="#moabb-analysis-meta-analysis-combine-pvalues" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.meta_analysis.combine_pvalues">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.meta_analysis.</span></span><span class="sig-name descname"><span class="pre">combine_pvalues</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsubs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/meta_analysis.py#L272-L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.meta_analysis.combine_pvalues" title="Permalink to this definition">#</a></dt>
+<dd><p>Combine p-values for meta-analysis statistics.</p>
+<p>Function that takes pvals from each experiments and number of subjects to
+return meta-analysis significance using Stouffer’s method</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>p</strong> (<em>DataFrame</em>) – p-values for 2 pipelines computed on different datasets</p></li>
+<li><p><strong>nsubs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – average number of subject per datasets</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>pval</strong> – Estimatation of the combined p-value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
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+  <section id="moabb-analysis-meta-analysis-compute-dataset-statistics">
+<h1>moabb.analysis.meta_analysis.compute_dataset_statistics<a class="headerlink" href="#moabb-analysis-meta-analysis-compute-dataset-statistics" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.meta_analysis.compute_dataset_statistics">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.meta_analysis.</span></span><span class="sig-name descname"><span class="pre">compute_dataset_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perm_cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/meta_analysis.py#L211-L246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.meta_analysis.compute_dataset_statistics" title="Permalink to this definition">#</a></dt>
+<dd><p>Compute meta-analysis statistics from results dataframe.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>df</strong> (<em>DataFrame</em>) – results obtained by an evaluation</p></li>
+<li><p><strong>perm_cutoff</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=20</em>) – threshold value for using permutation or Wilcoxon tests</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>stats</strong> – Table of effect and p-values for each dataset and all pipelines</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>DataFrame</p>
+</dd>
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+  <section id="moabb-analysis-meta-analysis-find-significant-differences">
+<h1>moabb.analysis.meta_analysis.find_significant_differences<a class="headerlink" href="#moabb-analysis-meta-analysis-find-significant-differences" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.meta_analysis.find_significant_differences">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.meta_analysis.</span></span><span class="sig-name descname"><span class="pre">find_significant_differences</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perm_cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/meta_analysis.py#L298-L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.meta_analysis.find_significant_differences" title="Permalink to this definition">#</a></dt>
+<dd><p>Compute differences between pipelines across datasets.</p>
+<p>Compute matrices of p-values and effects for all algorithms over all datasets via
+combined p-values and combined effects methods</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>df</strong> (<em>DataFrame</em>) – Table of effect and p-values for each dataset and all pipelines, returned by
+compute_dataset_statistics</p></li>
+<li><p><strong>perm_cutoff</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=20</em>) – threshold value  to stop using permutation tests, which can be very expensive
+computationally, using Wilcoxon rank-sum test instead</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>dfP</strong> (<em>DataFrame of shape (n_pipelines, n_pipelines)</em>) – p-values per algorithm pairs</p></li>
+<li><p><strong>dfT</strong> (<em>DataFrame of shape (n_pipelines, n_pipelines)</em>) – signed standardized mean differences</p></li>
+</ul>
+</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.analysis.meta_analysis.find_significant_differences"><code class="docutils literal notranslate"><span class="pre">find_significant_differences()</span></code></a></li>
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+  <section id="moabb-analysis-plotting-meta-analysis-plot">
+<h1>moabb.analysis.plotting.meta_analysis_plot<a class="headerlink" href="#moabb-analysis-plotting-meta-analysis-plot" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.plotting.meta_analysis_plot">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.plotting.</span></span><span class="sig-name descname"><span class="pre">meta_analysis_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stats_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alg1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alg2</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/plotting.py#L238-L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.plotting.meta_analysis_plot" title="Permalink to this definition">#</a></dt>
+<dd><p>Meta-analysis to compare two algorithms across several datasets.</p>
+<p>A meta-analysis style plot that shows the standardized effect with
+confidence intervals over all datasets for two algorithms.
+Hypothesis is that alg1 is larger than alg2</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>stats_df</strong> (<em>DataFrame</em>) – DataFrame generated by compute_dataset_statistics</p></li>
+<li><p><strong>alg1</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Name of first pipeline</p></li>
+<li><p><strong>alg2</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Name of second pipeline</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>fig</strong> – Pyplot handle</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Figure</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-analysis-plotting-meta-analysis-plot">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.meta_analysis_plot</span></code><a class="headerlink" href="#examples-using-moabb-analysis-plotting-meta-analysis-plot" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-analysis-plotting-meta-analysis-plot">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.meta_analysis_plot</span></code></a></li>
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+  <section id="moabb-analysis-plotting-paired-plot">
+<h1>moabb.analysis.plotting.paired_plot<a class="headerlink" href="#moabb-analysis-plotting-paired-plot" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.plotting.paired_plot">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.plotting.</span></span><span class="sig-name descname"><span class="pre">paired_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alg1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alg2</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/plotting.py#L146-L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.plotting.paired_plot" title="Permalink to this definition">#</a></dt>
+<dd><p>Generate a figure with a paired plot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>DataFrame</em>) – dataframe obtained from evaluation</p></li>
+<li><p><strong>alg1</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Name of a member of column data.pipeline</p></li>
+<li><p><strong>alg2</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Name of a member of column data.pipeline</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>fig</strong> – Pyplot handle</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Figure</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-analysis-plotting-paired-plot">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.paired_plot</span></code><a class="headerlink" href="#examples-using-moabb-analysis-plotting-paired-plot" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-analysis-plotting-paired-plot">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.paired_plot</span></code></a></li>
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+  <section id="moabb-analysis-plotting-score-plot">
+<h1>moabb.analysis.plotting.score_plot<a class="headerlink" href="#moabb-analysis-plotting-score-plot" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.plotting.score_plot">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.plotting.</span></span><span class="sig-name descname"><span class="pre">score_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipelines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orientation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vertical'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/plotting.py#L30-L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.plotting.score_plot" title="Permalink to this definition">#</a></dt>
+<dd><p>Plot scores for all pipelines and all datasets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>output</em><em> of </em><em>Results.to_dataframe</em><em>(</em><em>)</em>) – results on datasets</p></li>
+<li><p><strong>pipelines</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – pipelines to include in this plot</p></li>
+<li><p><strong>orientation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>default=&quot;vertical&quot;</em>) – plot orientation, could be [“vertical”, “v”, “horizontal”, “h”]</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>fig</strong> (<em>Figure</em>) – Pyplot handle</p></li>
+<li><p><strong>color_dict</strong> (<em>dict</em>) – Dictionary with the facecolor</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-analysis-plotting-score-plot">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.score_plot</span></code><a class="headerlink" href="#examples-using-moabb-analysis-plotting-score-plot" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Benchmarking with MOABB with Grid Search" src="../_images/sphx_glr_plot_benchmark_grid_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_grid_search.html#sphx-glr-auto-examples-plot-benchmark-grid-search-py"><span class="std std-ref">Benchmarking with MOABB with Grid Search</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB with Grid Search</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="moabb-analysis-plotting-summary-plot">
+<h1>moabb.analysis.plotting.summary_plot<a class="headerlink" href="#moabb-analysis-plotting-summary-plot" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.analysis.plotting.summary_plot">
+<span class="sig-prename descclassname"><span class="pre">moabb.analysis.plotting.</span></span><span class="sig-name descname"><span class="pre">summary_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sig_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">effect_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">simplify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/analysis/plotting.py#L178-L235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.analysis.plotting.summary_plot" title="Permalink to this definition">#</a></dt>
+<dd><p>Significance matrix to compare pipelines.</p>
+<p>Visualize significances as a heatmap with green/grey/red for significantly
+higher/significantly lower.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sig_df</strong> (<em>DataFrame</em>) – DataFrame of pipeline x pipeline where each value is a p-value,</p></li>
+<li><p><strong>effect_df</strong> (<em>DataFrame</em>) – DataFrame where each value is an effect size</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>fig</strong> – Pyplot handle</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Figure</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-analysis-plotting-summary-plot">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.summary_plot</span></code><a class="headerlink" href="#examples-using-moabb-analysis-plotting-summary-plot" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.analysis.plotting.summary_plot"><code class="docutils literal notranslate"><span class="pre">summary_plot()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-analysis-plotting-summary-plot">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.analysis.plotting.summary_plot</span></code></a></li>
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+  <section id="moabb-benchmark">
+<h1>moabb.benchmark<a class="headerlink" href="#moabb-benchmark" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.benchmark">
+<span class="sig-prename descclassname"><span class="pre">moabb.</span></span><span class="sig-name descname"><span class="pre">benchmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pipelines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./pipelines/'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">paradigms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./results/'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./benchmark/'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs_evaluation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contexts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_datasets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_datasets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/benchmark.py#L34-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.benchmark" title="Permalink to this definition">#</a></dt>
+<dd><p>Run benchmarks for selected pipelines and datasets.</p>
+<p>Load from saved pipeline configurations to determine associated paradigms. It is
+possible to include or exclude specific datasets and to choose the type of
+evaluation.</p>
+<p>If particular paradigms are mentioned through select_paradigms, only the pipelines corresponding to those paradigms
+will be run. If no paradigms are mentioned, all pipelines will be run.</p>
+<p>Pipelines stored in a file named braindecode_xxx.py will be recognized as Braindecode architectures
+and they will receive epochs as input, instead of numpy array.</p>
+<p>To define the include_datasets or exclude_dataset, you could start from the full dataset list,
+using for example the following code:
+&gt; # Choose your paradigm
+&gt; p = moabb.paradigms.SSVEP()
+&gt; # Get the class names
+&gt; print(p.datasets)
+&gt; # Get the dataset code
+&gt; print([d.code for d in p.datasets])</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pipelines</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Folder containing the pipelines to evaluate</p></li>
+<li><p><strong>evaluations</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – If to restrict the types of evaluations to be run. By default, all 3 base types are run
+Can be a list of these elements [“WithinSession”, “CrossSession”, “CrossSubject”]</p></li>
+<li><p><strong>paradigms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – To restrict the paradigms on which evaluations should be run.
+Can be a list of these elements [‘LeftRightImagery’, ‘MotorImagery’, ‘FilterBankSSVEP’, ‘SSVEP’,
+‘FilterBankMotorImagery’]</p></li>
+<li><p><strong>results</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Folder to store the results</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force evaluation of cached pipelines</p></li>
+<li><p><strong>output</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Folder to store the analysis results</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of threads to use for running parallel jobs</p></li>
+<li><p><strong>n_jobs_evaluation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for evaluation, processing in parallel the within session,
+cross-session or cross-subject.</p></li>
+<li><p><strong>plot</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Plot results after computing</p></li>
+<li><p><strong>contexts</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – File path to context.yml file that describes context parameters.
+If none, assumes all defaults. Must contain an entry for all
+paradigms described in the pipelines.</p></li>
+<li><p><strong>include_datasets</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>Dataset object</em>) – Datasets (dataset.code or object) to include in the benchmark run.
+By default, all suitable datasets are included. If both include_datasets
+and exclude_datasets are specified, raise an error.</p></li>
+<li><p><strong>exclude_datasets</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>Dataset object</em>) – Datasets to exclude from the benchmark run</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>eval_results</strong> – Results of benchmark for all considered paradigms</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>DataFrame</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+</dd></dl>
+
+<section id="examples-using-moabb-benchmark">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.benchmark</span></code><a class="headerlink" href="#examples-using-moabb-benchmark" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to track the CO2 footprint using `CodeCarbon library &lt;https..."><img alt="Benchmarking with MOABB showing the CO2 footprint" src="../_images/sphx_glr_example_codecarbon_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB showing the CO2 footprint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Benchmarking with MOABB with Grid Search" src="../_images/sphx_glr_plot_benchmark_grid_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_grid_search.html#sphx-glr-auto-examples-plot-benchmark-grid-search-py"><span class="std std-ref">Benchmarking with MOABB with Grid Search</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB with Grid Search</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="moabb-datasets-alexmi">
+<h1>moabb.datasets.AlexMI<a class="headerlink" href="#moabb-datasets-alexmi" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.AlexMI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">AlexMI</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/alex_mi.py#L12-L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.AlexMI" title="Permalink to this definition">#</a></dt>
+<dd><p>Alex Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>AlexMI</p></td>
+<td><p>8</p></td>
+<td><p>16</p></td>
+<td><p>3</p></td>
+<td><p>20</p></td>
+<td><p>3s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Motor imagery dataset from the PhD dissertation of A. Barachant <a class="reference internal" href="#r209ccc4ad4dd-1" id="id1">[1]</a>.</p>
+<p>This Dataset contains EEG recordings from 8 subjects, performing 2 task of
+motor imagination (right hand, feet or rest). Data have been recorded at
+512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8,
+P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.</p>
+<p>File are provided in MNE raw file format. A stimulation channel encoding
+the timing of the motor imagination. The start of a trial is encoded as 1,
+then the actual start of the motor imagination is encoded with 2 for
+imagination of a right hand movement, 3 for imagination of both feet
+movement and 4 with a rest trial.</p>
+<p>The duration of each trial is 3 second. There is 20 trial of each class.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r209ccc4ad4dd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Barachant, A., 2012. Commande robuste d’un effecteur par une
+interface cerveau machine EEG asynchrone (Doctoral dissertation,
+Université de Grenoble).
+<a class="reference external" href="https://tel.archives-ouvertes.fr/tel-01196752">https://tel.archives-ouvertes.fr/tel-01196752</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.AlexMI.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/alex_mi.py#L62-L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.AlexMI.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-alexmi">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.AlexMI</span></code><a class="headerlink" href="#examples-using-moabb-datasets-alexmi" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The Brain Imaging Data Structure (BIDS) format is standard for storing neuroimaging data. It fo..."><img alt="Convert a MOABB dataset to BIDS" src="../_images/sphx_glr_noplot_bids_conversion_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/noplot_bids_conversion.html#sphx-glr-auto-examples-noplot-bids-conversion-py"><span class="std std-ref">Convert a MOABB dataset to BIDS</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convert a MOABB dataset to BIDS</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+  <section id="moabb-datasets-bi2012">
+<h1>moabb.datasets.BI2012<a class="headerlink" href="#moabb-datasets-bi2012" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BI2012">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BI2012</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Online</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L415-L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2012" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset BI2012 from a “Brain Invaders” experiment.</p>
+<p>Dataset following the setup from <a class="reference internal" href="#rd420735ae6ab-1" id="id1">[1]</a> carried-out at University of
+Grenoble Alpes.</p>
+<p>This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing
+the Brain Invaders, a visual P300 Brain-Computer Interface inspired by the famous vintage
+video game Space Invaders (Taito, Tokyo, Japan). The visual P300 is an event-related
+potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG
+data were recorded by 16 electrodes in an experiment that took place in the GIPSA-lab,
+Grenoble, France, in 2012). A full description of the experiment is available in <a class="reference internal" href="#rd420735ae6ab-1" id="id2">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Principal Investigator<span class="colon">:</span></dt>
+<dd class="field-odd"><p>B.Sc. Gijsbrecht Franciscus Petrus Van Veen</p>
+</dd>
+<dt class="field-even">Technical Supervisors<span class="colon">:</span></dt>
+<dd class="field-even"><p>Ph.D. Alexandre Barachant, Eng. Anton Andreev, Eng. Grégoire Cattan,
+Eng. Pedro. L. C. Rodrigues</p>
+</dd>
+<dt class="field-odd">Scientific Supervisor<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Ph.D. Marco Congedo</p>
+</dd>
+<dt class="field-even">ID of the dataset<span class="colon">:</span></dt>
+<dd class="field-even"><p>BI.EEG.2012-GIPSA</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2012</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2012</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2012</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.6.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd420735ae6ab-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>)</span>
+<p>Van Veen, G., Barachant, A., Andreev, A., Cattan, G., Rodrigues, P. C., &amp;
+Congedo, M. (2019). Building Brain Invaders: EEG data of an experimental validation.
+arXiv preprint arXiv:1905.05182.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.BI2012.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L476-L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2012.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2012"><code class="docutils literal notranslate"><span class="pre">BI2012</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2012.data_path"><code class="docutils literal notranslate"><span class="pre">BI2012.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-bi2013a">
+<h1>moabb.datasets.BI2013a<a class="headerlink" href="#moabb-datasets-bi2013a" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BI2013a">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BI2013a</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">NonAdaptive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Adaptive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Online</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L482-L582"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2013a" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset BI2013a from a “Brain Invaders” experiment.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>BI2013a</p></td>
+<td><p>24</p></td>
+<td><p>16</p></td>
+<td><p>3200 NT / 640 T</p></td>
+<td><p>1s</p></td>
+<td><p>512Hz</p></td>
+<td><p>(1-7)8 s|(8-24)1s</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset following the setup from <a class="reference internal" href="#r20000b838d47-1" id="id1">[1]</a> carried-out at University of
+Grenoble Alpes.</p>
+<p>This dataset concerns an experiment carried out at GIPSA-lab
+(University of Grenoble Alpes, CNRS, Grenoble-INP) in 2013.
+The recordings concerned 24 subjects in total. Subjects 1 to 7 participated
+to eight sessions, run in different days, subject 8 to 24 participated to
+one session. Each session consisted in two runs, one in a Non-Adaptive
+(classical) and one in an Adaptive (calibration-less) mode of operation.
+The order of the runs was randomized for each session. In both runs there
+was a Training (calibration) phase and an Online phase, always passed in
+this order. In the non-Adaptive run the data from the Training phase was
+used for classifying the trials on the Online phase using the training-test
+version of the MDM algorithm <a class="reference internal" href="#r20000b838d47-2" id="id2">[2]</a>. In the Adaptive run, the data from the
+training phase was not used at all, instead the classifier was initialized
+with generic class geometric means and continuously adapted to the incoming
+data using the Riemannian method explained in <a class="reference internal" href="#r20000b838d47-2" id="id3">[2]</a>. Subjects were completely
+blind to the mode of operation and the two runs appeared to them identical.</p>
+<p>In the Brain Invaders P300 paradigm, a repetition is composed of 12
+flashes, of which 2 include the Target symbol (Target flashes) and 10 do
+not (non-Target flash). Please see <a class="reference internal" href="#r20000b838d47-3" id="id4">[3]</a> for a description of the paradigm.
+For this experiment, in the Training phases the number of flashes is fixed
+(80 Target flashes and 400 non-Target flashes). In the Online phases the
+number of Target and non-Target still are in a ratio 1/5, however their
+number is variable because the Brain Invaders works with a fixed number of
+game levels, however the number of repetitions needed to destroy the target
+(hence to proceed to the next level) depends on the user’s performance
+<a class="reference internal" href="#r20000b838d47-2" id="id5">[2]</a>. In any case, since the classes are unbalanced, an appropriate score
+must be used for quantifying the performance of classification methods
+(e.g., balanced accuracy, AUC methods, etc).</p>
+<p>Data were acquired with a Nexus (TMSi, The Netherlands) EEG amplifier:</p>
+<ul class="simple">
+<li><p>Sampling Frequency: 512 samples per second</p></li>
+<li><p>Digital Filter: no</p></li>
+<li><p>Electrodes:  16 wet Silver/Silver Chloride electrodes positioned at
+FP1, FP2, F5, AFz, F6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2
+according to the 10/20 international system.</p></li>
+<li><p>Reference: left ear-lobe.</p></li>
+<li><p>Ground: N/A.</p></li>
+</ul>
+<dl class="field-list simple">
+<dt class="field-odd">Principal Investigators<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Erwan Vaineau, Dr. Alexandre Barachant</p>
+</dd>
+<dt class="field-even">Scientific Supervisor<span class="colon">:</span></dt>
+<dd class="field-even"><p>Dr. Marco Congedo</p>
+</dd>
+<dt class="field-odd">Technical Supervisor<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Anton Andreev</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r20000b838d47-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Vaineau, E., Barachant, A., Andreev, A., Rodrigues, P. C.,
+Cattan, G. &amp; Congedo, M. (2019). Brain invaders adaptive
+versus non-adaptive P300 brain-computer interface dataset.
+arXiv preprint arXiv:1904.09111.</p>
+</div>
+<div class="citation" id="r20000b838d47-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id3">2</a>,<a role="doc-backlink" href="#id5">3</a>)</span>
+<p>Barachant A, Congedo M (2014) A Plug &amp; Play P300 BCI using
+Information Geometry.
+arXiv:1409.0107.</p>
+</div>
+<div class="citation" id="r20000b838d47-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">3</a><span class="fn-bracket">]</span></span>
+<p>Congedo M, Goyat M, Tarrin N, Ionescu G, Rivet B,Varnet L, Rivet B,
+Phlypo R, Jrad N, Acquadro M, Jutten C (2011) “Brain Invaders”: a
+prototype of an open-source P300-based video game working with the
+OpenViBE platform. Proc. IBCI Conf., Graz, Austria, 280-283.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2013a</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2013a</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2013a</span></code> will be removed in  version 1.1.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.BI2013a.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L579-L582"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2013a.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2013a.data_path"><code class="docutils literal notranslate"><span class="pre">BI2013a.data_path()</span></code></a></li>
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+<h1>moabb.datasets.BI2014a<a class="headerlink" href="#moabb-datasets-bi2014a" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BI2014a">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BI2014a</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L585-L641"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2014a" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset BI2014a from a “Brain Invaders” experiment.</p>
+<p>This dataset contains electroencephalographic (EEG) recordings of 71 subjects
+playing to a visual P300 Brain-Computer Interface (BCI) videogame named Brain Invaders.
+The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target)
+that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded
+using 16 active dry electrodes with up to three game sessions. The experiment took place
+at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available
+at <a class="reference internal" href="#re21d09e260cc-1" id="id1">[1]</a>. The ID of this dataset is BI2014a.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Investigators<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p>
+</dd>
+<dt class="field-even">Technical Support<span class="colon">:</span></dt>
+<dd class="field-even"><p>Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues,
+M. Sc. Violette Gautheret</p>
+</dd>
+<dt class="field-odd">Scientific Supervisor<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Ph.D. Marco Congedo</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2014a</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2014a</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2014a</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.6.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="re21d09e260cc-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Korczowski, L., Ostaschenko, E., Andreev, A., Cattan, G., Rodrigues, P. L. C.,
+Gautheret, V., &amp; Congedo, M. (2019). Brain Invaders calibration-less P300-based
+BCI using dry EEG electrodes Dataset (BI2014a).
+<a class="reference external" href="https://hal.archives-ouvertes.fr/hal-02171575">https://hal.archives-ouvertes.fr/hal-02171575</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.BI2014a.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L638-L641"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2014a.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-bi2014a">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.BI2014a</span></code><a class="headerlink" href="#examples-using-moabb-datasets-bi2014a" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 5: Creating a dataset class"><img alt="Tutorial 5: Creating a dataset class" src="../_images/sphx_glr_noplot_tutorial_5_build_a_custom_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html#sphx-glr-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"><span class="std std-ref">Tutorial 5: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 5: Creating a dataset class</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2014a"><code class="docutils literal notranslate"><span class="pre">BI2014a</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2014a.data_path"><code class="docutils literal notranslate"><span class="pre">BI2014a.data_path()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-datasets-bi2014a">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.BI2014a</span></code></a></li>
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+  <section id="moabb-datasets-bi2014b">
+<h1>moabb.datasets.BI2014b<a class="headerlink" href="#moabb-datasets-bi2014b" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BI2014b">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BI2014b</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L644-L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2014b" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset BI2014b from a “Brain Invaders” experiment.</p>
+<p>This dataset contains electroencephalographic (EEG) recordings of 38 subjects playing in
+pair (19 pairs) to the multi-user version of a visual P300-based Brain-Computer Interface (BCI)
+named Brain Invaders. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target,
+35 Non-Target) that are flashed pseudo-randomly to elicit a P300 response, an evoked-potential
+appearing about 300ms after stimulation onset. EEG data were recorded using 32 active wet
+electrodes per subjects (total: 64 electrodes) during three randomized conditions
+(Solo1, Solo2, Collaboration). The experiment took place at GIPSA-lab, Grenoble, France, in 2014.
+A full description of the experiment is available at <a class="reference internal" href="#re7e87b144863-1" id="id1">[1]</a>. The ID of this dataset is BI2014b.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Investigators<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p>
+</dd>
+<dt class="field-even">Technical Support<span class="colon">:</span></dt>
+<dd class="field-even"><p>Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues,
+M. Sc. Violette Gautheret</p>
+</dd>
+<dt class="field-odd">Scientific Supervisor<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Ph.D. Marco Congedo</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2014b</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2014b</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2014b</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.6.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="re7e87b144863-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Korczowski, L., Ostaschenko, E., Andreev, A., Cattan, G., Rodrigues, P. L. C.,
+Gautheret, V., &amp; Congedo, M. (2019). Brain Invaders Solo versus Collaboration:
+Multi-User P300-Based Brain-Computer Interface Dataset (BI2014b).
+<a class="reference external" href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.BI2014b.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L698-L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2014b.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2014b"><code class="docutils literal notranslate"><span class="pre">BI2014b</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2014b.data_path"><code class="docutils literal notranslate"><span class="pre">BI2014b.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-bi2015a">
+<h1>moabb.datasets.BI2015a<a class="headerlink" href="#moabb-datasets-bi2015a" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BI2015a">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BI2015a</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L704-L762"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2015a" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset BI2015a from a “Brain Invaders” experiment.</p>
+<p>This dataset contains electroencephalographic (EEG) recordings
+of 43 subjects playing to a visual P300 Brain-Computer Interface (BCI)
+videogame named Brain Invaders. The interface uses the oddball paradigm
+on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed
+pseudo-randomly to elicit the P300 response. EEG data were recorded using
+32 active wet electrodes with three conditions: flash duration 50ms, 80ms
+or 110ms. The experiment took place at GIPSA-lab, Grenoble, France, in 2015.
+A full description of the experiment is available at <a class="reference internal" href="#r6934c183e98b-1" id="id1">[1]</a>. The ID of this
+dataset is BI2015a.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Investigators<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Eng. Louis Korczowski, B. Sc. Martine Cederhout</p>
+</dd>
+<dt class="field-even">Technical Support<span class="colon">:</span></dt>
+<dd class="field-even"><p>Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues,
+M. Sc. Violette Gautheret</p>
+</dd>
+<dt class="field-odd">Scientific Supervisor<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Ph.D. Marco Congedo</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2015a</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2015a</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2015a</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.6.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6934c183e98b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Korczowski, L., Cederhout, M., Andreev, A., Cattan, G., Rodrigues, P. L. C.,
+Gautheret, V., &amp; Congedo, M. (2019). Brain Invaders calibration-less P300-based
+BCI with modulation of flash duration Dataset (BI2015a)
+<a class="reference external" href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.BI2015a.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L759-L762"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2015a.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2015a"><code class="docutils literal notranslate"><span class="pre">BI2015a</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BI2015a.data_path"><code class="docutils literal notranslate"><span class="pre">BI2015a.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-bi2015b">
+<h1>moabb.datasets.BI2015b<a class="headerlink" href="#moabb-datasets-bi2015b" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BI2015b">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BI2015b</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L765-L826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2015b" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset BI2015b from a “Brain Invaders” experiment.</p>
+<blockquote>
+<div></div></blockquote>
+<p>This dataset contains electroencephalographic (EEG) recordings
+of 44 subjects playing in pair to the multi-user version of a visual
+P300 Brain-Computer Interface (BCI) named Brain Invaders. The interface
+uses the oddball paradigm on a grid of 36 symbols (1 or 2 Target,
+35 or 34 Non-Target) that are flashed pseudo-randomly to elicit the
+P300 response. EEG data were recorded using 32 active wet electrodes
+per subjects (total: 64 electrodes) during four randomised conditions
+(Cooperation 1-Target, Cooperation 2-Targets, Competition 1-Target,
+Competition 2-Targets). The experiment took place at GIPSA-lab, Grenoble,
+France, in 2015. A full description of the experiment is available at
+A full description of the experiment is available at <a class="reference internal" href="#rc9de5d3c7a3b-1" id="id1">[1]</a>. The ID of this
+dataset is BI2015a.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Investigators<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Eng. Louis Korczowski, B. Sc. Martine Cederhout</p>
+</dd>
+<dt class="field-even">Technical Support<span class="colon">:</span></dt>
+<dd class="field-even"><p>Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues,
+M. Sc. Violette Gautheret</p>
+</dd>
+<dt class="field-odd">Scientific Supervisor<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Ph.D. Marco Congedo</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2015b</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2015b</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2015b</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.6.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc9de5d3c7a3b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Korczowski, L., Cederhout, M., Andreev, A., Cattan, G., Rodrigues, P. L. C.,
+Gautheret, V., &amp; Congedo, M. (2019). Brain Invaders Cooperative versus Competitive:
+Multi-User P300-based Brain-Computer Interface Dataset (BI2015b)
+<a class="reference external" href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.BI2015b.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L823-L826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BI2015b.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
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+  <section id="moabb-datasets-bnci2014-001">
+<h1>moabb.datasets.BNCI2014_001<a class="headerlink" href="#moabb-datasets-bnci2014-001" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2014_001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014_001</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L755-L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2014_001" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2014-001 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <a class="reference internal" href="#r55ebd47d0fe7-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
+paradigm consisted of four different motor imagery tasks, namely the imag-
+ination of movement of the left hand (class 1), right hand (class 2), both
+feet (class 3), and tongue (class 4).  Two sessions on different days were
+recorded for each subject.  Each session is comprised of 6 runs separated
+by short breaks.  One run consists of 48 trials (12 for each of the four
+possible classes), yielding a total of 288 trials per session.</p>
+<p>The subjects were sitting in a comfortable armchair in front of a computer
+screen.  At the beginning of a trial ( t = 0 s), a fixation cross appeared
+on the black screen.  In addition, a short acoustic warning tone was
+presented.  After two seconds ( t = 2 s), a cue in the form of an arrow
+pointing either to the left, right, down or up (corresponding to one of the
+four classes left hand, right hand, foot or tongue) appeared and stayed on
+the screen for 1.25 s.  This prompted the subjects to perform the desired
+motor imagery task.  No feedback was provided.  The subjects were ask to
+carry out the motor imagery task until the fixation cross disappeared from
+the screen at t = 6 s.</p>
+<p>Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm)
+were used to record the EEG; the montage is shown in Figure 3 left.  All
+signals were recorded monopolarly with the left mastoid serving as
+reference and the right mastoid as ground. The signals were sampled with.
+250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of
+the amplifier was set to 100 μV . An additional 50 Hz notch filter was
+enabled to suppress line noise</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r55ebd47d0fe7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2014_001</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code> will be removed in  version 1.1.</p>
+</div>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-bnci2014-001">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.BNCI2014_001</span></code><a class="headerlink" href="#examples-using-moabb-datasets-bnci2014-001" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to track the CO2 footprint using `CodeCarbon library &lt;https..."><img alt="Benchmarking with MOABB showing the CO2 footprint" src="../_images/sphx_glr_example_codecarbon_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB showing the CO2 footprint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="moabb-datasets-bnci2014-002">
+<h1>moabb.datasets.BNCI2014_002<a class="headerlink" href="#moabb-datasets-bnci2014-002" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2014_002">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014_002</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L819-L878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2014_002" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2014-002 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Motor Imagery Dataset from <a class="reference internal" href="#r48fbbb5504eb-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>The session consisted of eight runs, five of them for training and three
+with feedback for validation.  One run was composed of 20 trials.  Taken
+together, we recorded 50 trials per class for training and 30 trials per
+class for validation.  Participants had the task of performing sustained (5
+seconds) kinaesthetic motor imagery (MI) of the right hand and of the feet
+each as instructed by the cue. At 0 s, a white colored cross appeared on
+screen, 2 s later a beep sounded to catch the participant’s attention. The
+cue was displayed from 3 s to 4 s. Participants were instructed to start
+with MI as soon as they recognized the cue and to perform the indicated MI
+until the cross disappeared at 8 s. A rest period with a random length
+between 2 s and 3 s was presented between trials. Participants did not
+receive feedback during training.  Feedback was presented in form of a
+white
+coloured bar-graph.  The length of the bar-graph reflected the amount of
+correct classifications over the last second.  EEG was measured with a
+biosignal amplifier and active Ag/AgCl electrodes (g.USBamp, g.LADYbird,
+Guger Technologies OG, Schiedlberg, Austria) at a sampling rate of 512 Hz.
+The electrodes placement was designed for obtaining three Laplacian
+derivations.  Center electrodes at positions C3, Cz, and C4 and four
+additional electrodes around each center electrode with a distance of 2.5
+cm, 15 electrodes total.  The reference electrode was mounted on the left
+mastoid and the ground electrode on the right mastoid.  The 13 participants
+were aged between 20 and 30 years, 8 naive to the task, and had no known
+medical or neurological diseases.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r48fbbb5504eb-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Steyrl, D., Scherer, R., Faller, J. and Müller-Putz, G.R., 2016.
+Random forests in non-invasive sensorimotor rhythm brain-computer
+interfaces: a practical and convenient non-linear classifier.
+Biomedical Engineering/Biomedizinische Technique, 61(1), pp.77-86.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2014_002</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2014002</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2014002</span></code> will be removed in  version 1.1.</p>
+</div>
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+  <section id="moabb-datasets-bnci2014-004">
+<h1>moabb.datasets.BNCI2014_004<a class="headerlink" href="#moabb-datasets-bnci2014-004" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2014_004">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014_004</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L881-L961"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2014_004" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2014-004 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset B from BCI Competition 2008.</p>
+<p><strong>Dataset description</strong></p>
+<p>This data set consists of EEG data from 9 subjects of a study published in
+<a class="reference internal" href="#ra513946bbff8-1" id="id1">[1]</a>. The subjects were right-handed, had normal or corrected-to-normal
+vision and were paid for participating in the experiments.
+All volunteers were sitting in an armchair, watching a flat screen monitor
+placed approximately 1 m away at eye level. For each subject 5 sessions
+are provided, whereby the first two sessions contain training data without
+feedback (screening), and the last three sessions were recorded with
+feedback.</p>
+<p>Three bipolar recordings (C3, Cz, and C4) were recorded with a sampling
+frequency of 250 Hz.They were bandpass- filtered between 0.5 Hz and 100 Hz,
+and a notch filter at 50 Hz was enabled.  The placement of the three
+bipolar recordings (large or small distances, more anterior or posterior)
+were slightly different for each subject (for more details see [1]).
+The electrode position Fz served as EEG ground. In addition to the EEG
+channels, the electrooculogram (EOG) was recorded with three monopolar
+electrodes.</p>
+<p>The cue-based screening paradigm consisted of two classes,
+namely the motor imagery (MI) of left hand (class 1) and right hand
+(class 2).
+Each subject participated in two screening sessions without feedback
+recorded on two different days within two weeks.
+Each session consisted of six runs with ten trials each and two classes of
+imagery.  This resulted in 20 trials per run and 120 trials per session.
+Data of 120 repetitions of each MI class were available for each person in
+total.  Prior to the first motor im- agery training the subject executed
+and imagined different movements for each body part and selected the one
+which they could imagine best (e. g., squeezing a ball or pulling a brake).</p>
+<p>Each trial started with a fixation cross and an additional short acoustic
+warning tone (1 kHz, 70 ms).  Some seconds later a visual cue was presented
+for 1.25 seconds.  Afterwards the subjects had to imagine the corresponding
+hand movement over a period of 4 seconds.  Each trial was followed by a
+short break of at least 1.5 seconds.  A randomized time of up to 1 second
+was added to the break to avoid adaptation</p>
+<p>For the three online feedback sessions four runs with smiley feedback
+were recorded, whereby each run consisted of twenty trials for each type of
+motor imagery.  At the beginning of each trial (second 0) the feedback (a
+gray smiley) was centered on the screen.  At second 2, a short warning beep
+(1 kHz, 70 ms) was given. The cue was presented from second 3 to 7.5. At
+second 7.5 the screen went blank and a random interval between 1.0 and 2.0
+seconds was added to the trial.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra513946bbff8-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>R. Leeb, F. Lee, C. Keinrath, R. Scherer, H. Bischof,
+G. Pfurtscheller. Brain-computer communication: motivation, aim,
+and impact of exploring a virtual apartment. IEEE Transactions on
+Neural Systems and Rehabilitation Engineering 15, 473–482, 2007</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2014_004</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2014004</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2014004</span></code> will be removed in  version 1.1.</p>
+</div>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-bnci2014-004">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.BNCI2014_004</span></code><a class="headerlink" href="#examples-using-moabb-datasets-bnci2014-004" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+  <section id="moabb-datasets-bnci2014-008">
+<h1>moabb.datasets.BNCI2014_008<a class="headerlink" href="#moabb-datasets-bnci2014-008" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2014_008">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014_008</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L964-L1032"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2014_008" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2014-008 P300 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset from <a class="reference internal" href="#re1b59a9063f6-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>This dataset represents a complete record of P300 evoked potentials
+using a paradigm originally described by Farwell and Donchin <a class="reference internal" href="#re1b59a9063f6-2" id="id2">[2]</a>.
+In these sessions, 8 users with amyotrophic lateral sclerosis (ALSO)
+focused on one out of 36 different characters. The objective in this
+contest is to predict the correct character in each of the provided
+character selection epochs.</p>
+<p>We included in the study a total of eight volunteers, all naïve to BCI
+training. Scalp EEG signals were recorded (g.MOBILAB, g.tec, Austria)
+from eight channels according to 10–10 standard (Fz, Cz, Pz, Oz, P3, P4,
+PO7 and PO8) using active electrodes (g.Ladybird, g.tec, Austria).
+All channels were referenced to the right earlobe and grounded to the left
+mastoid. The EEG signal was digitized at 256 Hz and band-pass filtered
+between 0.1 and 30 Hz.</p>
+<p>Participants were required to copy spell seven predefined words of five
+characters each (runs), by controlling a P300 matrix speller. Rows and
+columns on the interface were randomly intensified for 125ms, with an
+inter stimulus interval (ISI) of 125ms, yielding a 250 ms lag between the
+appearance of two stimuli (stimulus onset asynchrony, SOA).</p>
+<p>In the first three runs (15 trials in total) EEG data was stored to
+perform a calibration of the BCI classifier. Thus no feedback was provided
+to the participant up to this point. A stepwise linear discriminant
+analysis (SWLDA) was applied to the data from the three calibration runs
+(i.e., runs 1–3) to determine the classifier weights (i.e., classifier
+coefficients). These weights were then applied during the subsequent four
+testing runs (i.e., runs 4–7) when participants were provided with
+feedback.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="re1b59a9063f6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>A. Riccio, L. Simione, F. Schettini, A. Pizzimenti, M. Inghilleri,
+M. O. Belardinelli, D. Mattia, and F. Cincotti (2013). Attention
+and P300-based BCI performance in people with amyotrophic lateral
+sclerosis. Front. Hum. Neurosci., vol. 7:, pag. 732.</p>
+</div>
+<div class="citation" id="re1b59a9063f6-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>L. A. Farwell and E. Donchin, Talking off the top of your head:
+toward a mental prosthesis utilizing eventrelated
+brain potentials, Electroencephalogr. Clin. Neurophysiol.,
+vol. 70, n. 6, pagg. 510–523, 1988.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2014_008</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2014008</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2014008</span></code> will be removed in  version 1.1.</p>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BNCI2014_008"><code class="docutils literal notranslate"><span class="pre">BNCI2014_008</span></code></a></li>
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+  <section id="moabb-datasets-bnci2014-009">
+<h1>moabb.datasets.BNCI2014_009<a class="headerlink" href="#moabb-datasets-bnci2014-009" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2014_009">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014_009</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L1035-L1094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2014_009" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2014-009 P300 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset from <a class="reference internal" href="#r68ef25884b54-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>This dataset presents a complete record of P300 evoked potentials
+using two different paradigms: a paradigm based on the P300 Speller in
+overt attention condition and a paradigm based used in convert attention
+condition. In these sessions, 10 healthy subjects focused on one out of 36
+different characters. The objective was to predict the correct character
+in each of the provided character selection epochs.
+(Note: right now only the overt attention data is available via MOABB)</p>
+<p>In the first interface, cues are organized in a 6×6 matrix and each
+character is always visible on the screen and spatially separated from the
+others. By design, no fixation cue is provided, as the subject is expected
+to gaze at the target character. Stimulation consists in the
+intensification of whole lines (rows or columns) of six characters.</p>
+<p>Ten healthy subjects (10 female, mean age = 26.8 ± 5.6, table I) with
+previous experience with P300-based BCIs attended 3 recording sessions.
+Scalp EEG potentials were measured using 16 Ag/AgCl electrodes that
+covered the left, right and central scalp (Fz, FCz, Cz, CPz, Pz, Oz, F3,
+F4, C3, C4, CP3, CP4, P3, P4, PO7, PO8) per the 10-10 standard. Each
+electrode was referenced to the linked earlobes and grounded to the
+right mastoid. The EEG was acquired at 256 Hz, high pass- and low
+pass-filtered with cutoff frequencies of 0.1 Hz and 20 Hz, respectively.
+Each subject attended 4 recording sessions. During each session,
+the subject performed three runs with each of the stimulation interfaces.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r68ef25884b54-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>P Aricò, F Aloise, F Schettini, S Salinari, D Mattia and F Cincotti
+(2013). Influence of P300 latency jitter on event related potential-
+based brain–computer interface performance. Journal of Neural
+Engineering, vol. 11, number 3.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2014_009</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2014009</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2014009</span></code> will be removed in  version 1.1.</p>
+</div>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-bnci2014-009">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.BNCI2014_009</span></code><a class="headerlink" href="#examples-using-moabb-datasets-bnci2014-009" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BNCI2014_009"><code class="docutils literal notranslate"><span class="pre">BNCI2014_009</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-datasets-bnci2014-009">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.BNCI2014_009</span></code></a></li>
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+  <section id="moabb-datasets-bnci2015-001">
+<h1>moabb.datasets.BNCI2015_001<a class="headerlink" href="#moabb-datasets-bnci2015-001" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2015_001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2015_001</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L1097-L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2015_001" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2015-001 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset from <a class="reference internal" href="#r0887fbcc8a8f-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>We acquired the EEG from three Laplacian derivations, 3.5 cm (center-to-
+center) around the electrode positions (according to International 10-20
+System of Electrode Placement) C3 (FC3, C5, CP3 and C1), Cz (FCz, C1, CPz
+and C2) and C4 (FC4, C2, CP4 and C6).  The acquisition hardware was a
+g.GAMMAsys active electrode system along with a g.USBamp amplifier (g.tec,
+Guger Tech- nologies OEG, Graz, Austria).  The system sampled at 512 Hz,
+with a bandpass filter between 0.5 and 100 Hz and a notch filter at 50 Hz.
+The order of the channels in the data is FC3, FCz, FC4, C5, C3, C1, Cz, C2,
+C4, C6, CP3, CPz, CP4.</p>
+<p>The task for the user was to perform sustained right hand versus both feet
+movement imagery starting from the cue (second 3) to the end of the cross
+period (sec- and 8).  A trial started with 3 s of reference period,
+followed by a brisk audible cue and a visual cue (arrow right for right
+hand, arrow down for both feet) from second 3 to 4.25.
+The activity period, where the users received feedback, lasted from
+second 4 to 8. There was a random 2 to 3 s pause between the trials.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0887fbcc8a8f-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>J. Faller, C. Vidaurre, T. Solis-Escalante, C. Neuper and R.
+Scherer (2012). Autocalibration and recurrent adaptation: Towards a
+plug and play online ERD- BCI.  IEEE Transactions on Neural Systems
+and Rehabilitation Engineering, 20(3), 313-319.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2015_001</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2015001</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2015001</span></code> will be removed in  version 1.1.</p>
+</div>
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BNCI2015_001"><code class="docutils literal notranslate"><span class="pre">BNCI2015_001</span></code></a></li>
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+  <section id="moabb-datasets-bnci2015-003">
+<h1>moabb.datasets.BNCI2015_003<a class="headerlink" href="#moabb-datasets-bnci2015-003" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2015_003">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2015_003</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L1153-L1193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2015_003" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2015-003 P300 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset from <a class="reference internal" href="#r98ea6c134749-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>This dataset contains recordings from 10 subjects performing a visual P300
+task for spelling. Results were published in <a class="reference internal" href="#r98ea6c134749-1" id="id2">[1]</a>. Sampling frequency was
+256 Hz and there were 8 electrodes (‘Fz’, ‘Cz’, ‘P3’, ‘Pz’, ‘P4’, ‘PO7’,
+‘Oz’, ‘PO8’) which were referenced to the right earlobe. Each subject
+participated in only one session. For more information, see <a class="reference internal" href="#r98ea6c134749-1" id="id3">[1]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r98ea6c134749-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>)</span>
+<p>C. Guger, S. Daban, E. Sellers, C. Holzner, G. Krausz,
+R. Carabalona, F. Gramatica, and G. Edlinger (2009). How many
+people are able to control a P300-based brain-computer interface
+(BCI)?. Neuroscience Letters, vol. 462, pp. 94–98.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2015_003</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2015003</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2015003</span></code> will be removed in  version 1.1.</p>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BNCI2015_003"><code class="docutils literal notranslate"><span class="pre">BNCI2015_003</span></code></a></li>
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+  <section id="moabb-datasets-bnci2015-004">
+<h1>moabb.datasets.BNCI2015_004<a class="headerlink" href="#moabb-datasets-bnci2015-004" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.BNCI2015_004">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2015_004</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci.py#L1196-L1267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.BNCI2015_004" title="Permalink to this definition">#</a></dt>
+<dd><p>BNCI 2015-004 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset from <a class="reference internal" href="#r92adba70080b-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>We provide EEG data recorded from nine users with disability (spinal cord
+injury and stroke) on two different days (sessions).  Users performed,
+follow- ing a cue-guided experimental paradigm, five distinct mental tasks
+(MT).  MTs include mental word association (condition WORD), mental
+subtraction (SUB), spatial navigation (NAV), right hand motor imagery
+(HAND) and
+feet motor imagery (FEET). Details on the experimental paradigm are
+summarized in Figure 1.  The session for a single subject consisted of 8
+runs resulting in 40 trials of each class for each day.  One single
+experimental run consisted of 25 cues, with 5 of each mental task.  Cues
+were presented in random order.</p>
+<p>EEG was recorded from 30 electrode channels placed on the scalp according
+to the international 10-20 system.  Electrode positions included channels
+AFz, F7, F3, Fz, F4, F8, FC3, FCz, FC4, T3, C3, Cz, C4, T4, CP3, CPz,CP4,
+P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO3, PO4, O1, and O2.  Reference and
+ground were placed at the left and right mastoid, respectively.  The g.tec
+GAMMAsys system with g.LADYbird active electrodes and two g.USBamp
+biosignal
+amplifiers (Guger Technologies, Graz, Austria) was used for recording.  EEG
+was band pass filtered 0.5-100 Hz (notch filter at 50 Hz) and sampled at a
+rate of 256 Hz.</p>
+<p>The duration of a single imagery trials is 10 s.  At t = 0 s, a cross was
+presented in the middle of the screen.  Participants were asked to relax
+and
+fixate the cross to avoid eye movements.  At t = 3 s, a beep was sounded to
+get the participant’s attention.  The cue indicating the requested imagery
+task, one out of five graphical symbols, was presented from t = 3 s to t =
+4.25 s.  At t = 10 s, a second beep was sounded and the fixation-cross
+disappeared, which indicated the end of the trial.  A variable break
+(inter-trial-interval, ITI) lasting between 2.5 s and 3.5 s occurred
+before
+the start of the next trial.  Participants were asked to avoid movements
+during the imagery period, and to move and blink during the
+ITI. Experimental runs began and ended with a blank screen (duration 4 s)</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r92adba70080b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Scherer R, Faller J, Friedrich EVC, Opisso E, Costa U, Kübler A, et
+al. (2015) Individually Adapted Imagery Improves Brain-Computer
+Interface Performance in End-Users with Disability. PLoS ONE 10(5).
+<a class="reference external" href="https://doi.org/10.1371/journal.pone.0123727">https://doi.org/10.1371/journal.pone.0123727</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BNCI2015_004</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">BNCI2015004</span></code>. <code class="docutils literal notranslate"><span class="pre">BNCI2015004</span></code> will be removed in  version 1.1.</p>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.BNCI2015_004"><code class="docutils literal notranslate"><span class="pre">BNCI2015_004</span></code></a></li>
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+  <section id="moabb-datasets-castillosburstvep100">
+<h1>moabb.datasets.CastillosBurstVEP100<a class="headerlink" href="#moabb-datasets-castillosburstvep100" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.CastillosBurstVEP100">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">CastillosBurstVEP100</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/castillos2023.py#L291-L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.CastillosBurstVEP100" title="Permalink to this definition">#</a></dt>
+<dd><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023)</p>
+<p>Dataset <a class="reference internal" href="#r426cfb774f38-1" id="id1">[1]</a> from the study on burst-VEP <a class="reference internal" href="#r426cfb774f38-2" id="id2">[2]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Sessions</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>#Trial classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>#Epoch classes</p></th>
+<th class="head"><p>#Epochs / class</p></th>
+<th class="head"><p>Codes</p></th>
+<th class="head"><p>Presentation rate</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>CastillosBurstVEP100</p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>5820NT/1200T</p></td>
+<td><p>Burst-CVEP</p></td>
+<td><p>60Hz</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded
+using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface
+brain activity. The 32 electrodes were placed following the 10–20 international system on a BrainProduct Acticap. The
+ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The
+impedance of all electrodes was brought below 25kOhm prior to recording onset. Once equipped with the EEG system,
+volunteers were asked to focus on four targets that were cued sequentially in a random order for 0.5 s, followed by a
+2.2 s stimulation phase, before a 0.7 s inter-trial period. The cue sequence for each trial was pseudo-random and
+different for each block. After each block, a pause was observed and subjects had to press the space bar to continue.
+The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence x
+40% or 100%). The task was implemented in Python using the Psychopy toolbox. The four discs were all
+150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 x 1080 pixels, 265
+cd/m2, and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were
+asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100%
+amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude. Each
+stimulus was presented three times in a pseudo-random order. Following the presentation of each stimulus, participants
+were presented with three 11-points scales and were asked to rate the visual comfort, visual tiredness, and
+intrusiveness using a mouse. In total, participants completed 12 ratings (3 repetitions x 4 types of stimuli) for
+each of the three scales.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r426cfb774f38-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset).
+DOI: <a class="reference external" href="https://doi.org/10.5281/zenodo.8255618">https://doi.org/10.5281/zenodo.8255618</a></p>
+</div>
+<div class="citation" id="r426cfb774f38-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais. Burst c-VEP Based BCI: Optimizing stimulus
+design for enhanced classification with minimal calibration data and improved user experience,NeuroImage,Volume 284,
+2023,120446,ISSN 1053-8119
+DOI: <a class="reference external" href="https://doi.org/10.1016/j.neuroimage.2023.120446">https://doi.org/10.1016/j.neuroimage.2023.120446</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.1.0.</span></p>
+</div>
+</dd></dl>
+
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+  <section id="moabb-datasets-castillosburstvep40">
+<h1>moabb.datasets.CastillosBurstVEP40<a class="headerlink" href="#moabb-datasets-castillosburstvep40" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.CastillosBurstVEP40">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">CastillosBurstVEP40</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/castillos2023.py#L353-L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.CastillosBurstVEP40" title="Permalink to this definition">#</a></dt>
+<dd><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023)</p>
+<p>Dataset <a class="reference internal" href="#rde16200d260f-1" id="id1">[1]</a> from the study on burst-VEP <a class="reference internal" href="#rde16200d260f-2" id="id2">[2]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Sessions</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>#Trial classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>#Epoch classes</p></th>
+<th class="head"><p>#Epochs / class</p></th>
+<th class="head"><p>Codes</p></th>
+<th class="head"><p>Presentation rate</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>CastillosBurstVEP40</p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>5820NT/1200T</p></td>
+<td><p>Burst-CVEP</p></td>
+<td><p>60Hz</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded
+using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface
+brain activity. The 32 electrodes were placed following the 10–20 international system on a BrainProduct Acticap. The
+ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The
+impedance of all electrodes was brought below 25kOhm prior to recording onset. Once equipped with the EEG system,
+volunteers were asked to focus on four targets that were cued sequentially in a random order for 0.5 s, followed by a
+2.2 s stimulation phase, before a 0.7 s inter-trial period. The cue sequence for each trial was pseudo-random and
+different for each block. After each block, a pause was observed and subjects had to press the space bar to continue.
+The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence x
+40% or 100%). The task was implemented in Python using the Psychopy toolbox. The four discs were all
+150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 x 1080 pixels, 265
+cd/m2, and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were
+asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100%
+amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude. Each
+stimulus was presented three times in a pseudo-random order. Following the presentation of each stimulus, participants
+were presented with three 11-points scales and were asked to rate the visual comfort, visual tiredness, and
+intrusiveness using a mouse. In total, participants completed 12 ratings (3 repetitions x 4 types of stimuli) for
+each of the three scales.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rde16200d260f-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset).
+DOI: <a class="reference external" href="https://doi.org/10.5281/zenodo.8255618">https://doi.org/10.5281/zenodo.8255618</a></p>
+</div>
+<div class="citation" id="rde16200d260f-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais. Burst c-VEP Based BCI: Optimizing stimulus
+design for enhanced classification with minimal calibration data and improved user experience,NeuroImage,Volume 284,
+2023,120446,ISSN 1053-8119
+DOI: <a class="reference external" href="https://doi.org/10.1016/j.neuroimage.2023.120446">https://doi.org/10.1016/j.neuroimage.2023.120446</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.1.0.</span></p>
+</div>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+  <section id="moabb-datasets-castilloscvep100">
+<h1>moabb.datasets.CastillosCVEP100<a class="headerlink" href="#moabb-datasets-castilloscvep100" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.CastillosCVEP100">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">CastillosCVEP100</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/castillos2023.py#L415-L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.CastillosCVEP100" title="Permalink to this definition">#</a></dt>
+<dd><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023)</p>
+<p>Dataset <a class="reference internal" href="#rfa6c5d763236-1" id="id1">[1]</a> from the study on burst-VEP <a class="reference internal" href="#rfa6c5d763236-2" id="id2">[2]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Sessions</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>#Trial classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>#Epoch classes</p></th>
+<th class="head"><p>#Epochs / class</p></th>
+<th class="head"><p>Codes</p></th>
+<th class="head"><p>Presentation rate</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>CastillosCVEP100</p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>500Hz</p></td>
+<td><p>32</p></td>
+<td><p>2.2s</p></td>
+<td><p>4</p></td>
+<td><p>15/15/15/15</p></td>
+<td><p>2</p></td>
+<td><p>3525NT/3495T</p></td>
+<td><p>m-sequence</p></td>
+<td><p>60Hz</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded
+using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface
+brain activity. The 32 electrodes were placed following the 10–20 international system on a BrainProduct Acticap. The
+ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The
+impedance of all electrodes was brought below 25kOhm prior to recording onset. Once equipped with the EEG system,
+volunteers were asked to focus on four targets that were cued sequentially in a random order for 0.5 s, followed by a
+2.2 s stimulation phase, before a 0.7 s inter-trial period. The cue sequence for each trial was pseudo-random and
+different for each block. After each block, a pause was observed and subjects had to press the space bar to continue.
+The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence x
+40% or 100%). The task was implemented in Python using the Psychopy toolbox. The four discs were all
+150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 x 1080 pixels, 265
+cd/m2, and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were
+asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100%
+amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude. Each
+stimulus was presented three times in a pseudo-random order. Following the presentation of each stimulus, participants
+were presented with three 11-points scales and were asked to rate the visual comfort, visual tiredness, and
+intrusiveness using a mouse. In total, participants completed 12 ratings (3 repetitions x 4 types of stimuli) for
+each of the three scales.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rfa6c5d763236-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset).
+DOI: <a class="reference external" href="https://doi.org/10.5281/zenodo.8255618">https://doi.org/10.5281/zenodo.8255618</a></p>
+</div>
+<div class="citation" id="rfa6c5d763236-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais. Burst c-VEP Based BCI: Optimizing stimulus
+design for enhanced classification with minimal calibration data and improved user experience,NeuroImage,Volume 284,
+2023,120446,ISSN 1053-8119
+DOI: <a class="reference external" href="https://doi.org/10.1016/j.neuroimage.2023.120446">https://doi.org/10.1016/j.neuroimage.2023.120446</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.1.0.</span></p>
+</div>
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+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.CastillosCVEP40">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">CastillosCVEP40</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/castillos2023.py#L478-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.CastillosCVEP40" title="Permalink to this definition">#</a></dt>
+<dd><p>c-VEP and Burst-VEP dataset from Castillos et al. (2023)</p>
+<p>Dataset <a class="reference internal" href="#rd14e8cf35fc6-1" id="id1">[1]</a> from the study on burst-VEP <a class="reference internal" href="#rd14e8cf35fc6-2" id="id2">[2]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded
+using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface
+brain activity. The 32 electrodes were placed following the 10–20 international system on a BrainProduct Acticap. The
+ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The
+impedance of all electrodes was brought below 25kOhm prior to recording onset. Once equipped with the EEG system,
+volunteers were asked to focus on four targets that were cued sequentially in a random order for 0.5 s, followed by a
+2.2 s stimulation phase, before a 0.7 s inter-trial period. The cue sequence for each trial was pseudo-random and
+different for each block. After each block, a pause was observed and subjects had to press the space bar to continue.
+The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence x
+40% or 100%). The task was implemented in Python using the Psychopy toolbox. The four discs were all
+150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 x 1080 pixels, 265
+cd/m2, and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were
+asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100%
+amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude. Each
+stimulus was presented three times in a pseudo-random order. Following the presentation of each stimulus, participants
+were presented with three 11-points scales and were asked to rate the visual comfort, visual tiredness, and
+intrusiveness using a mouse. In total, participants completed 12 ratings (3 repetitions x 4 types of stimuli) for
+each of the three scales.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd14e8cf35fc6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset).
+DOI: <a class="reference external" href="https://doi.org/10.5281/zenodo.8255618">https://doi.org/10.5281/zenodo.8255618</a></p>
+</div>
+<div class="citation" id="rd14e8cf35fc6-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais. Burst c-VEP Based BCI: Optimizing stimulus
+design for enhanced classification with minimal calibration data and improved user experience,NeuroImage,Volume 284,
+2023,120446,ISSN 1053-8119
+DOI: <a class="reference external" href="https://doi.org/10.1016/j.neuroimage.2023.120446">https://doi.org/10.1016/j.neuroimage.2023.120446</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.1.0.</span></p>
+</div>
+</dd></dl>
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+  <section id="moabb-datasets-cattan2019-phmd">
+<h1>moabb.datasets.Cattan2019_PHMD<a class="headerlink" href="#moabb-datasets-cattan2019-phmd" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Cattan2019_PHMD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Cattan2019_PHMD</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/phmd_ml.py#L16-L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cattan2019_PHMD" title="Permalink to this definition">#</a></dt>
+<dd><p>Passive Head Mounted Display with Music Listening dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>We describe the experimental procedures for a dataset that we have made publicly available
+at <a class="reference external" href="https://doi.org/10.5281/zenodo.2617084">https://doi.org/10.5281/zenodo.2617084</a> in mat (Mathworks, Natick, USA) and csv formats.
+This dataset contains electroencephalographic recordings of 12 subjects listening to music
+with and without a passive head-mounted display, that is, a head-mounted display which does
+not include any electronics at the exception of a smartphone. The electroencephalographic
+headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking
+place in the GIPSA-lab, Grenoble, France, in 2017 (Cattan and al, 2018).
+The ID of this dataset is PHMDML.EEG.2017-GIPSA.</p>
+<p><strong>full description of the experiment</strong>
+<a class="reference external" href="https://hal.archives-ouvertes.fr/hal-02085118">https://hal.archives-ouvertes.fr/hal-02085118</a></p>
+<p><strong>Link to the data</strong>
+<a class="reference external" href="https://doi.org/10.5281/zenodo.2617084">https://doi.org/10.5281/zenodo.2617084</a></p>
+<p><strong>Authors</strong>
+Principal Investigator: Eng. Grégoire Cattan
+Technical Supervisors: Eng. Pedro L. C. Rodrigues
+Scientific Supervisor: Dr. Marco Congedo</p>
+<p><strong>ID of the dataset</strong>
+PHMDML.EEG.2017-GIPSA</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">Cattan2019_PHMD</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">HeadMountedDisplay</span></code>. <code class="docutils literal notranslate"><span class="pre">HeadMountedDisplay</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.0.0.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rf4e3a2801e75-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<p>G. Cattan, P. L. Coelho Rodrigues, and M. Congedo,
+‘Passive Head-Mounted Display Music-Listening EEG dataset’,
+Gipsa-Lab ; IHMTEK, Research Report 2, Mar. 2019. doi: 10.5281/zenodo.2617084.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Cattan2019_PHMD.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/phmd_ml.py#L115-L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cattan2019_PHMD.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-cattan2019-phmd">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.Cattan2019_PHMD</span></code><a class="headerlink" href="#examples-using-moabb-datasets-cattan2019-phmd" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to perform spectral analysis on epochs extracted from a specific ..."><img alt="Spectral analysis of the trials" src="../_images/sphx_glr_noplot_phmd_ml_spectrum_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/noplot_phmd_ml_spectrum.html#sphx-glr-auto-examples-noplot-phmd-ml-spectrum-py"><span class="std std-ref">Spectral analysis of the trials</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Spectral analysis of the trials</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+  <section id="moabb-datasets-cattan2019-vr">
+<h1>moabb.datasets.Cattan2019_VR<a class="headerlink" href="#moabb-datasets-cattan2019-vr" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Cattan2019_VR">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Cattan2019_VR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">virtual_reality</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">screen_display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L829-L952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cattan2019_VR" title="Permalink to this definition">#</a></dt>
+<dd><p>Dataset of an EEG-based BCI experiment in Virtual Reality using P300.</p>
+<p>We describe the experimental procedures for a dataset that we have made publicly
+available at <a class="reference external" href="https://doi.org/10.5281/zenodo.2605204">https://doi.org/10.5281/zenodo.2605204</a> in mat (Mathworks, Natick, USA)
+and csv formats <a class="reference internal" href="#r4b90cbefe65c-1" id="id1">[1]</a>. This dataset contains electroencephalographic recordings on 21
+subjects doing a visual P300 experiment on non-VR (PC display) and VR (virtual
+reality). The visual P300 is an event-related potential elicited by a visual
+stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed
+in order to compare the use of a P300-based brain-computer interface on a PC and
+with a virtual reality headset, concerning the physiological, subjective and
+performance aspects. The brain-computer interface is based on electroencephalography
+(EEG). EEG data were recorded thanks to 16 electrodes. The virtual reality headset
+consisted of a passive head-mounted display, that is, a head-mounted display which
+does not include any electronics at the exception of a smartphone. A full description
+of the experiment is available at <a class="reference external" href="https://hal.archives-ouvertes.fr/hal-02078533">https://hal.archives-ouvertes.fr/hal-02078533</a>.</p>
+<p>See the example <cite>plot_vr_pc_p300_different_epoch_size</cite> to compare the performance
+between PC and VR.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>virtual_reality</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default False</em><em>)</em>) – if True, return runs corresponding to P300 experiment on virtual reality.</p></li>
+<li><p><strong>screen_display</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default True</em><em>)</em>) – if True, return runs corresponding to P300 experiment on personal computer.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">Cattan2019_VR</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">VirtualReality</span></code>. <code class="docutils literal notranslate"><span class="pre">VirtualReality</span></code> will be removed in  version 1.1.</p>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4b90cbefe65c-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>G. Cattan, A. Andreev, P. L. C. Rodrigues, and M. Congedo (2019).
+Dataset of an EEG-based BCI experiment in Virtual Reality and
+on a Personal Computer. Research Report, GIPSA-lab; IHMTEK.
+<a class="reference external" href="https://doi.org/10.5281/zenodo.2605204">https://doi.org/10.5281/zenodo.2605204</a></p>
+</div>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Cattan2019_VR.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L901-L904"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cattan2019_VR.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Cattan2019_VR.get_block_repetition">
+<span class="sig-name descname"><span class="pre">get_block_repetition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paradigm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subjects</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repetition_list</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/braininvaders.py#L906-L952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cattan2019_VR.get_block_repetition" title="Permalink to this definition">#</a></dt>
+<dd><p>Select data for all provided subjects, blocks and repetitions. Each
+subject has 12 blocks of 5 repetitions.</p>
+<p>The returned data is a dictionary with the following structure:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;subject_id&#39;</span> <span class="p">:</span>
+            <span class="p">{</span><span class="s1">&#39;session_id&#39;</span><span class="p">:</span>
+                <span class="p">{</span><span class="s1">&#39;run_id&#39;</span><span class="p">:</span> <span class="n">raw</span><span class="p">}</span>
+            <span class="p">}</span>
+        <span class="p">}</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseDataset.get_data</span></code></p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subjects</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of subject number</p></li>
+<li><p><strong>block_list</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of block number (from 0 to 11)</p></li>
+<li><p><strong>repetition_list</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of repetition number inside a block (from 0 to 4)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>data</strong> – dict containing the raw data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-cattan2019-vr">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.Cattan2019_VR</span></code><a class="headerlink" href="#examples-using-moabb-datasets-cattan2019-vr" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="======================================= Changing epoch size in P300 VR dataset ================..."><img alt="Example of P300 classification with different epoch size." src="../_images/sphx_glr_noplot_vr_pc_p300_different_epoch_size_thumb.png" />
+<p><a class="reference external" href="https://neurotechx.github.io/moabb/auto_examples/noplot_vr_pc_p300_different_epoch_size.html#sphx-glr-auto-examples-noplot-vr-pc-p300-different-epoch-size-py" title="(in moabb v1.0.0)"><span class="xref std std-ref">sphx_glr_auto_examples_noplot_vr_pc_p300_different_epoch_size.py</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Example of P300 classification with different epoch size.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 5: Creating a dataset class"><img alt="Tutorial 5: Creating a dataset class" src="../_images/sphx_glr_noplot_tutorial_5_build_a_custom_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html#sphx-glr-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"><span class="std std-ref">Tutorial 5: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 5: Creating a dataset class</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Cattan2019_VR"><code class="docutils literal notranslate"><span class="pre">Cattan2019_VR</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Cattan2019_VR.data_path"><code class="docutils literal notranslate"><span class="pre">Cattan2019_VR.data_path()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Cattan2019_VR.get_block_repetition"><code class="docutils literal notranslate"><span class="pre">Cattan2019_VR.get_block_repetition()</span></code></a></li>
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+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-datasets-cattan2019-vr">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.Cattan2019_VR</span></code></a></li>
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+  <section id="moabb-datasets-cho2017">
+<h1>moabb.datasets.Cho2017<a class="headerlink" href="#moabb-datasets-cho2017" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Cho2017">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Cho2017</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/gigadb.py#L19-L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cho2017" title="Permalink to this definition">#</a></dt>
+<dd><p>Motor Imagery dataset from Cho et al 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Cho2017</p></td>
+<td><p>52</p></td>
+<td><p>64</p></td>
+<td><p>2</p></td>
+<td><p>100</p></td>
+<td><p>3s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from the paper <a class="reference internal" href="#rfbfbd6dd436f-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>We conducted a BCI experiment for motor imagery movement (MI movement)
+of the left and right hands with 52 subjects (19 females, mean age ± SD
+age = 24.8 ± 3.86 years); Each subject took part in the same experiment,
+and subject ID was denoted and indexed as s1, s2, …, s52.
+Subjects s20 and s33 were both-handed, and the other 50 subjects
+were right-handed.</p>
+<p>EEG data were collected using 64 Ag/AgCl active electrodes.
+A 64-channel montage based on the international 10-10 system was used to
+record the EEG signals with 512 Hz sampling rates.
+The EEG device used in this experiment was the Biosemi ActiveTwo system.
+The BCI2000 system 3.0.2 was used to collect EEG data and present
+instructions (left hand or right hand MI). Furthermore, we recorded
+EMG as well as EEG simultaneously with the same system and sampling rate
+to check actual hand movements. Two EMG electrodes were attached to the
+flexor digitorum profundus and extensor digitorum on each arm.</p>
+<p>Subjects were asked to imagine the hand movement depending on the
+instruction given. Five or six runs were performed during the MI
+experiment. After each run, we calculated the classification
+accuracy over one run and gave the subject feedback to increase motivation.
+Between each run, a maximum 4-minute break was given depending on
+the subject’s demands.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rfbfbd6dd436f-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cho, H., Ahn, M., Ahn, S., Kwon, M. and Jun, S.C., 2017.
+EEG datasets for motor imagery brain computer interface.
+GigaScience. <a class="reference external" href="https://doi.org/10.1093/gigascience/gix034">https://doi.org/10.1093/gigascience/gix034</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Cho2017.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/gigadb.py#L127-L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Cho2017.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
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+<h1>moabb.datasets.DemonsP300<a class="headerlink" href="#moabb-datasets-demonsp300" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.DemonsP300">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">DemonsP300</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/neiry.py#L14-L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.DemonsP300" title="Permalink to this definition">#</a></dt>
+<dd><p>Visual P300 dataset recorded in Virtual Reality (VR) game Raccoons
+versus Demons.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>DemonsP300</p></td>
+<td><p>60</p></td>
+<td><p>8</p></td>
+<td><p>935 NT / 50 T</p></td>
+<td><p>1s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="admonition danger">
+<p class="admonition-title">Danger</p>
+<p>This dataset contains major unresolved issues and could removed in the near futur. Use it in a benchmark
+at your own risk. See <a class="github reference external" href="https://github.com/NeuroTechX/moabb/issues/216">NeuroTechX/moabb#216</a></p>
+</div>
+<p><strong>Dataset Description</strong></p>
+<p>We publish dataset of visual P300 BCI performed in Virtual Reality (VR) game Raccoons versus
+Demons (RvD). Data contains reach labels incorporating information about stimulus chosen enabling us
+to estimate model’s confidence at each stimulus prediction stage.
+<cite>target</cite> channel contains standard P300 target/non-target labels,
+while <cite>mult_target</cite> channel contains multiclass labels (numbers of activated stimuli).</p>
+<p><strong>Participants</strong></p>
+<p>60 healthy participants (23 males) naive to BCI with mean age 28 years from 19 to 45 y.o. took part in the study.
+All subject signed informed consent and passed primary prerequisites on their health and condition.</p>
+<p><strong>Stimulation and EEG recording</strong></p>
+<p>The EEG was recorded with NVX-52 encephalograph (MCS, Zelenograd, Russia) at 500 Hz. We used 8 sponge
+electrodes (Cz, P3, P4, PO3, POz, PO4, O1, O2). Stimuli were presented with HTC Vive Pro VR headset with
+TTL hardware sync</p>
+<p><strong>Experimental procedure</strong></p>
+<p>Participants were asked to play the P300 BCI game in virtual reality.
+BCI was embedded into a game plot with the player posing as a forest warden.
+The player was supposed to feed animals and protect them from demons.
+Game mechanics consisted in demons jumping (visually activating),
+so player have to concentrate on one demon (chosen freely). That produced
+P300 response in time of the daemon jump. That was the way to trigger fireball
+towards a daemon predicted by classifier from EEG data.</p>
+<p>More info can be found in <a class="reference internal" href="#r35e223bfd2b6-1" id="id1">[1]</a> <a class="reference internal" href="#r35e223bfd2b6-2" id="id2">[2]</a> <a class="reference internal" href="#r35e223bfd2b6-3" id="id3">[3]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r35e223bfd2b6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Goncharenko V., Grigoryan R., and Samokhina A. (May 12, 2020),
+Raccoons vs Demons: multiclass labeled P300 dataset,
+<a class="reference external" href="https://arxiv.org/abs/2005.02251">https://arxiv.org/abs/2005.02251</a></p>
+</div>
+<div class="citation" id="r35e223bfd2b6-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Goncharenko V., Grigoryan R., and Samokhina A.,
+Approaches to multiclass classification of P300 potential datasets,
+Intelligent Data Processing: Theory and Applications:Book of abstract of
+the 13th International Conference, Moscow, 2020. — Moscow: Russian
+Academy of Sciences, 2020. — 472 p.ISBN 978-5-907366-16-9
+http://www.machinelearning.ru/wiki/images/3/31/Idp20.pdf</p>
+</div>
+<div class="citation" id="r35e223bfd2b6-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goncharenko V., Grigoryan R., and Samokhina A.,
+P300 potentials dataset and approaches to its processing,
+Труды 63-й Всероссийской научной конференции МФТИ. 23–29 ноября 2020
+года. Прикладные математика и информатика. —  Москва : МФТИ, 2020. – 334 с.
+ISBN 978-5-7417-0757-9
+<a class="reference external" href="https://mipt.ru/science/5top100/education/courseproposal/%D0%A4%D0%9F%D0%9C%D0%98%20%D1%84%D0%B8%D0%BD%D0%B0%D0%BB-compressed2.pdf">https://mipt.ru/science/5top100/education/courseproposal/%D0%A4%D0%9F%D0%9C%D0%98%20%D1%84%D0%B8%D0%BD%D0%B0%D0%BB-compressed2.pdf</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.DemonsP300.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/neiry.py#L189-L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.DemonsP300.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.DemonsP300.read_hdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_hdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/neiry.py#L134-L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.DemonsP300.read_hdf" title="Permalink to this definition">#</a></dt>
+<dd><p>Reads data from HDF file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array of <cite>_act_dtype</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.DemonsP300"><code class="docutils literal notranslate"><span class="pre">DemonsP300</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.DemonsP300.data_path"><code class="docutils literal notranslate"><span class="pre">DemonsP300.data_path()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.DemonsP300.read_hdf"><code class="docutils literal notranslate"><span class="pre">DemonsP300.read_hdf()</span></code></a></li>
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+  <section id="moabb-datasets-epflp300">
+<h1>moabb.datasets.EPFLP300<a class="headerlink" href="#moabb-datasets-epflp300" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.EPFLP300">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">EPFLP300</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/epfl.py#L18-L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.EPFLP300" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset from Hoffmann et al 2008.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>EPFLP300</p></td>
+<td><p>8</p></td>
+<td><p>32</p></td>
+<td><p>2753 NT / 551 T</p></td>
+<td><p>1s</p></td>
+<td><p>2048Hz</p></td>
+<td><p>4</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from the paper <a class="reference internal" href="#r8b72a14900a1-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>In the present work a six-choice P300 paradigm is tested using a population
+of five disabled and four able-bodied subjects. Six different images were
+flashed in random order with a stimulus interval of 400 ms. Users were
+facing a laptop screen on which six im- ages were displayed. The images
+showed a television, a telephone, a lamp, a door, a window, and a radio.</p>
+<p>The images were flashed in random sequences, one image at a time. Each
+flash of an image lasted for 100 ms and during the following 300 ms none of
+the images was flashed, i.e. the interstimulus interval was 400 ms. The EEG
+was recorded at 2048 Hz sampling rate from 32 electrodes placed at the
+standard positions of the 10-20 international system. The system was tested
+with five disabled and four healthy subjects. The disabled subjects were
+all wheelchair-bound but had varying communication and limb muscle control
+abilities (Subjects 1 to 5). In particular, Subject 5 was only able
+to perform extremely slow and relatively uncontrolled movements with hands
+and arms. Due to a severe hypophony and large fluctuations in the level of
+alertness, communication with subject 5 was very difficult, which is why
+its data is not available in this dataset. Subjects 6 to 9 were PhD
+students recruited from our laboratory (all male, age 30 ± 2.3).</p>
+<p>Each subject completed four recording sessions. The first two sessions were
+performed on one day and the last two sessions on another day. For all
+subjects the time between the first and the last session was less than two
+weeks. Each of the sessions consisted of six runs, one run for each of the
+six images. The duration of one run was approximately one minute and the
+duration of one session including setup of electrodes and short breaks
+between runs was approximately 30 minutes. One session comprised on average
+810 trials, and the whole data for one subject consisted on average of 3240
+trials.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8b72a14900a1-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hoffmann, U., Vesin, J-M., Ebrahimi, T., Diserens, K., 2008.
+An efficient P300-based brain-computer interfacefor disabled
+subjects. Journal of Neuroscience Methods .
+<a class="reference external" href="https://doi.org/10.1016/j.jneumeth.2007.03.005">https://doi.org/10.1016/j.jneumeth.2007.03.005</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.EPFLP300.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/epfl.py#L171-L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.EPFLP300.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
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+  <section id="moabb-datasets-grossewentrup2009">
+<h1>moabb.datasets.GrosseWentrup2009<a class="headerlink" href="#moabb-datasets-grossewentrup2009" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.GrosseWentrup2009">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">GrosseWentrup2009</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/mpi_mi.py#L14-L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.GrosseWentrup2009" title="Permalink to this definition">#</a></dt>
+<dd><p>Munich Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>GrosseWentrup2009</p></td>
+<td><p>10</p></td>
+<td><p>128</p></td>
+<td><p>2</p></td>
+<td><p>150</p></td>
+<td><p>7s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Motor imagery dataset from Grosse-Wentrup et al. 2009 <a class="reference internal" href="#r73167574105b-1" id="id1">[1]</a>.</p>
+<p>A trial started with the central display of a white fixation cross. After 3
+s, a white arrow was superimposed on the fixation cross, either pointing to
+the left or the right.
+Subjects were instructed to perform haptic motor imagery of the
+left or the right hand during display of the arrow, as indicated by the
+direction of the arrow. After another 7 s, the arrow was removed,
+indicating the end of the trial and start of the next trial. While subjects
+were explicitly instructed to perform haptic motor imagery with the
+specified hand, i.e., to imagine feeling instead of visualizing how their
+hands moved, the exact choice of which type of imaginary movement, i.e.,
+moving the fingers up and down, gripping an object, etc., was left
+unspecified.
+A total of 150 trials per condition were carried out by each subject,
+with trials presented in pseudorandomized order.</p>
+<p>Ten healthy subjects (S1–S10) participated in the experimental
+evaluation. Of these, two were females, eight were right handed, and their
+average age was 25.6 years with a standard deviation of 2.5 years. Subject
+S3 had already participated twice in a BCI experiment, while all other
+subjects were naive to BCIs. EEG was recorded at M=128 electrodes placed
+according to the extended 10–20 system. Data were recorded at 500 Hz with
+electrode Cz as reference. Four BrainAmp amplifiers were used for this
+purpose, using a temporal analog high-pass filter with a time constant of
+10 s. The data were re-referenced to common average reference
+offline. Electrode impedances were below 10 kΩ for all electrodes and
+subjects at the beginning of each recording session. No trials were
+rejected and no artifact correction was performed. For each subject, the
+locations of the 128 electrodes were measured in three dimensions using a
+Zebris ultrasound tracking system and stored for further offline analysis.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r73167574105b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Grosse-Wentrup, Moritz, et al. “Beamforming in noninvasive
+brain–computer interfaces.” IEEE Transactions on Biomedical
+Engineering 56.4 (2009): 1209-1219.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">GrosseWentrup2009</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">MunichMI</span></code>. <code class="docutils literal notranslate"><span class="pre">MunichMI</span></code> will be removed in  version 1.1.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.GrosseWentrup2009.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/mpi_mi.py#L90-L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.GrosseWentrup2009.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
+                </article>
+              
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+  <section id="moabb-datasets-huebner2017">
+<h1>moabb.datasets.Huebner2017<a class="headerlink" href="#moabb-datasets-huebner2017" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Huebner2017">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Huebner2017</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_slice_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_blocks_as_sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/huebner_llp.py#L116-L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Huebner2017" title="Permalink to this definition">#</a></dt>
+<dd><p>Learning from label proportions for a visual matrix speller (ERP)
+dataset from Hübner et al 2017 <a class="reference internal" href="#r0a211c89d39d-1" id="id1">[1]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Huebner2017</p></td>
+<td><p>13</p></td>
+<td><p>31</p></td>
+<td><p>364 NT / 112 T</p></td>
+<td><p>0.9s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>The subjects were asked to spell the sentence: “Franzy jagt im komplett verwahrlosten Taxi quer durch Freiburg”.
+The sentence was chosen because it contains each letter used in German at least once. Each subject spelled this
+sentence three times. The stimulus onset asynchrony (SOA) was 250 ms (corresponding to 15 frames on the LCD screen
+utilized) while the stimulus duration was 100 ms (corresponding to 6 frames on the LCD screen utilized). For each
+character, 68 highlighting events occurred and a total of 63 characters were spelled three times. This resulted in
+a total of 68 ⋅ 63 ⋅ 3 = 12852 EEG epochs per subject. Spelling one character took around 25 s including 4 s for
+cueing the current symbol, 17 s for highlighting and 4 s to provide feedback to the user. Assuming a perfect
+decoding, these timing constraints would allow for a maximum spelling speed of 2.4 characters per minute. Fig 1
+shows the complete experimental structure and how LLP is used to reconstruct average target and non-target ERP
+responses.</p>
+<p>Subjects were placed in a chair at 80 cm distance from a 24-inch flat screen. EEG signals from 31 passive Ag/AgCl
+electrodes (EasyCap) were recorded, which were placed approximately equidistantly according to the extended
+10–20 system, and whose impedances were kept below 20 kΩ. All channels were referenced against the nose and the
+ground was at FCz. The signals were registered by multichannel EEG amplifiers (BrainAmp DC, Brain Products) at a
+sampling rate of 1 kHz. To control for vertical ocular movements and eye blinks, we recorded with an EOG electrode
+placed below the right eye and referenced against the EEG channel Fp2 above the eye. In addition, pulse and
+breathing activity were recorded.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>interval</strong> (<em>array_like</em>) – range/interval in milliseconds in which the brain response/activity relative to an event/stimulus onset lies in.
+Default is set to [-.2, .7].</p></li>
+<li><p><strong>raw_slice_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>None</em>) – defines the crop offset in milliseconds before the first and after the last event (target or non-targeet) onset.
+Default None which crops with an offset 2,000 ms.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0a211c89d39d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017)
+Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.
+PLOS ONE 12(4): e0175856.
+<a class="reference external" href="https://doi.org/10.1371/journal.pone.0175856">https://doi.org/10.1371/journal.pone.0175856</a></p>
+</div>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.5.</span></p>
+</div>
+</dd></dl>
+
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+<h1>moabb.datasets.Huebner2018<a class="headerlink" href="#moabb-datasets-huebner2018" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Huebner2018">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Huebner2018</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_slice_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_blocks_as_sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/huebner_llp.py#L183-L239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Huebner2018" title="Permalink to this definition">#</a></dt>
+<dd><p>Mixture of LLP and EM for a visual matrix speller (ERP) dataset from
+Hübner et al 2018 <a class="reference internal" href="#r8f30fc0d0ace-1" id="id1">[1]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Huebner2018</p></td>
+<td><p>12</p></td>
+<td><p>31</p></td>
+<td><p>364 NT / 112 T</p></td>
+<td><p>0.9s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>Within a single session, a subject was asked to spell the beginning of a sentence in each of three blocks.The text
+consists of the 35 symbols “Franzy jagt im Taxi quer durch das ”. Each block, one of the three decoding
+algorithms (EM, LLP, MIX) was used in order to guess the attended symbol. The order of the blocks was
+pseudo-randomized over subjects, such that each possible order of the three decoding algorithms was used twice.
+This randomization should reduce systematic biases by order effects or temporal effects, e.g., due to fatigue or
+task-learning.</p>
+<p>A trial describes the process of spelling one character. Each of the 35 trials per block contained 68 highlighting
+events. The stimulus onset asynchrony (SOA) was 250 ms and the stimulus duration was 100 ms leading to an
+interstimulus interval (ISI) of 150 ms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>interval</strong> (<em>array_like</em>) – range/interval in milliseconds in which the brain response/activity relative to an event/stimulus onset lies in.
+Default is set to [-.2, .7].</p></li>
+<li><p><strong>raw_slice_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>None</em>) – defines the crop offset in milliseconds before the first and after the last event (target or non-targeet) onset.
+Default None which crops with an offset 2,000 ms.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8f30fc0d0ace-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Huebner, D., Verhoeven, T., Mueller, K. R., Kindermans, P. J., &amp; Tangermann, M. (2018).
+Unsupervised learning for brain-computer interfaces based on event-related potentials: Review and online comparison [research frontier].
+IEEE Computational Intelligence Magazine, 13(2), 66-77.
+<a class="reference external" href="https://doi.org/10.1109/MCI.2018.2807039">https://doi.org/10.1109/MCI.2018.2807039</a></p>
+</div>
+</div>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.5.</span></p>
+</div>
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+  <section id="moabb-datasets-kalunga2016">
+<h1>moabb.datasets.Kalunga2016<a class="headerlink" href="#moabb-datasets-kalunga2016" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Kalunga2016">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Kalunga2016</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_exo.py#L14-L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Kalunga2016" title="Permalink to this definition">#</a></dt>
+<dd><p>SSVEP Exo dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Kalunga2016</p></td>
+<td><p>12</p></td>
+<td><p>8</p></td>
+<td><p>4</p></td>
+<td><p>16</p></td>
+<td><p>2s</p></td>
+<td><p>256Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>SSVEP dataset from E. Kalunga PhD in University of Versailles <a class="reference internal" href="#r15adf1811e14-1" id="id1">[1]</a>.</p>
+<p>The datasets contains recording from 12 male and female subjects aged
+between 20 and 28 years. Informed consent was obtained from all subjects,
+each one has signed a form attesting her or his consent. The subject sits
+in an electric wheelchair, his right upper limb is resting on the
+exoskeleton. The exoskeleton is functional but is not used during the
+recording of this experiment.</p>
+<p>A panel of size 20x30 cm is attached on the left side of the chair, with
+3 groups of 4 LEDs blinking at different frequencies. Even if the panel
+is on the left side, the user could see it without moving its head. The
+subjects were asked to sit comfortably in the wheelchair and to follow the
+auditory instructions, they could move and blink freely.</p>
+<p>A sequence of trials is proposed to the user. A trial begin by an audio cue
+indicating which LED to focus on, or to focus on a fixation point set at an
+equal distance from all LEDs for the reject class. A trial lasts 5 seconds
+and there is a 3 second pause between each trial. The evaluation is
+conducted during a session consisting of 32 trials, with 8 trials for each
+frequency (13Hz, 17Hz and 21Hz) and 8 trials for the reject class, i.e.
+when the subject is not focusing on any specific blinking LED.</p>
+<p>There is between 2 and 5 sessions for each user, recorded on different
+days, by the same operators, on the same hardware and in the same
+conditions.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r15adf1811e14-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. “Online
+SSVEP-based BCI using Riemannian Geometry”. Neurocomputing, 2016.
+arXiv report: <a class="reference external" href="https://arxiv.org/abs/1501.03227">https://arxiv.org/abs/1501.03227</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">Kalunga2016</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">SSVEPExo</span></code>. <code class="docutils literal notranslate"><span class="pre">SSVEPExo</span></code> will be removed in  version 1.1.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Kalunga2016.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_exo.py#L82-L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Kalunga2016.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-kalunga2016">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.Kalunga2016</span></code><a class="headerlink" href="#examples-using-moabb-datasets-kalunga2016" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Kalunga2016.data_path"><code class="docutils literal notranslate"><span class="pre">Kalunga2016.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-lee2019-erp">
+<h1>moabb.datasets.Lee2019_ERP<a class="headerlink" href="#moabb-datasets-lee2019-erp" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Lee2019_ERP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Lee2019_ERP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resting_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1,</span> <span class="pre">2)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Lee2019.py#L290-L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Lee2019_ERP" title="Permalink to this definition">#</a></dt>
+<dd><p>BMI/OpenBMI dataset for P300.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Lee2019_ERP</p></td>
+<td><p>54</p></td>
+<td><p>62</p></td>
+<td><p>6900 NT / 1380 T</p></td>
+<td><p>1s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>2</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from Lee et al 2019 <a class="reference internal" href="#r9dac553bbf55-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>EEG signals were recorded with a sampling rate of 1,000 Hz and
+collected with 62 Ag/AgCl electrodes. The EEG amplifier used
+in the experiment was a BrainAmp (Brain Products; Munich,
+Germany). The channels were nasion-referenced and grounded
+to electrode AFz. Additionally, an EMG electrode recorded from
+each flexor digitorum profundus muscle with the olecranon
+used as reference. The EEG/EMG channel configuration and
+indexing numbers are described in Fig. 1. The impedances of the
+EEG electrodes were maintained below 10 k during the entire
+experiment.</p>
+<p>ERP paradigm
+The interface layout of the speller followed the typical design
+of a row-column speller. The six rows and six columns were
+configured with 36 symbols (A to Z, 1 to 9, and _). Each symbol
+was presented equally spaced. To enhance the
+signal quality, two additional settings were incorporated into
+the original row-column speller design, namely, random-set
+presentation and face stimuli. These additional settings
+help to elicit stronger ERP responses by minimizing adjacency
+distraction errors and by presenting a familiar face image. The
+stimulus-time interval was set to 80 ms, and the inter-stimulus
+interval (ISI) to 135 ms. A single iteration of stimulus presentation
+in all rows and columns was considered a sequence. Therefore,
+one sequence consisted of 12 stimulus flashes. A maximum
+of five sequences (i.e., 60 flashes) was allotted without prolonged
+inter-sequence intervals for each target character. After the end
+of five sequences, 4.5 s were given to the user for identifying, locating,
+and gazing at the next target character. The participant
+was instructed to attend to the target symbol by counting the
+number of times each target character had been flashed.
+In the training session, subjects were asked to copy-spell
+a given sentence, “NEURAL NETWORKS AND DEEP LEARNING”
+(33 characters including spaces) by gazing at the target character
+on the screen. The training session was performed in the offline
+condition, and no feedback was provided to the subject during
+the EEG recording. In the test session, subjects were instructed to
+copy-spell “PATTERN RECOGNITION MACHINE LEARNING”
+(36 characters including spaces), and the real-time EEG data were
+analyzed based on the classifier that was calculated from the
+training session data. The selected character from the subject’s
+current EEG data was displayed in the top left area of the screen
+at the end of the presentation (i.e., after five sequences).
+Per participant, the collected EEG data for the ERP experiment consisted
+of 1,980 and 2,160 trials (samples) for training and test phase, respectively.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default True</em><em>)</em>) – if True, return runs corresponding to the training/offline phase (see paper).</p></li>
+<li><p><strong>test_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default: False for MI and SSVEP paradigms</em><em>, </em><em>True for ERP</em><em>)</em>) – if True, return runs corresponding to the test/online phase (see paper). Beware that test_run
+for  MI and SSVEP do not have labels associated with trials: these runs could not be used in
+classification tasks.</p></li>
+<li><p><strong>resting_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default False</em><em>)</em>) – if True, return runs corresponding to the resting phases before and after recordings (see paper).</p></li>
+<li><p><strong>sessions</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default</em><em> [</em><em>1</em><em>,</em><em>2</em><em>]</em><em>)</em>) – the list of the sessions to load (2 available).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r9dac553bbf55-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E.,
+Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI
+toolbox for three BCI paradigms: An investigation into BCI
+illiteracy. GigaScience, 8(5), 1–16.
+<a class="reference external" href="https://doi.org/10.1093/gigascience/giz002">https://doi.org/10.1093/gigascience/giz002</a></p>
+</div>
+</div>
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Lee2019_ERP"><code class="docutils literal notranslate"><span class="pre">Lee2019_ERP</span></code></a></li>
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+  <section id="moabb-datasets-lee2019-mi">
+<h1>moabb.datasets.Lee2019_MI<a class="headerlink" href="#moabb-datasets-lee2019-mi" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Lee2019_MI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Lee2019_MI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resting_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1,</span> <span class="pre">2)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Lee2019.py#L220-L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Lee2019_MI" title="Permalink to this definition">#</a></dt>
+<dd><p>BMI/OpenBMI dataset for MI.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Lee2019_MI</p></td>
+<td><p>54</p></td>
+<td><p>62</p></td>
+<td><p>2</p></td>
+<td><p>100</p></td>
+<td><p>4s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>2</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from Lee et al 2019 <a class="reference internal" href="#r74de42efcdd6-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>EEG signals were recorded with a sampling rate of 1,000 Hz and
+collected with 62 Ag/AgCl electrodes. The EEG amplifier used
+in the experiment was a BrainAmp (Brain Products; Munich,
+Germany). The channels were nasion-referenced and grounded
+to electrode AFz. Additionally, an EMG electrode recorded from
+each flexor digitorum profundus muscle with the olecranon
+used as reference. The EEG/EMG channel configuration and
+indexing numbers are described in Fig. 1. The impedances of the
+EEG electrodes were maintained below 10 k during the entire
+experiment.</p>
+<p>MI paradigm
+The MI paradigm was designed following a well-established system protocol.
+For all blocks, the first 3 s of each trial began
+with a black fixation cross that appeared at the center of the
+monitor to prepare subjects for the MI task. Afterwards, the subject
+performed the imagery task of grasping with the appropriate
+hand for 4 s when the right or left arrow appeared as a visual cue.
+After each task, the screen remained blank for 6 s (± 1.5 s). The
+experiment consisted of training and test phases; each phase
+had 100 trials with balanced right and left hand imagery tasks.
+During the online test phase, the fixation cross appeared at the
+center of the monitor and moved right or left, according to the
+real-time classifier output of the EEG signal.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default True</em><em>)</em>) – if True, return runs corresponding to the training/offline phase (see paper).</p></li>
+<li><p><strong>test_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default: False for MI and SSVEP paradigms</em><em>, </em><em>True for ERP</em><em>)</em>) – if True, return runs corresponding to the test/online phase (see paper). Beware that test_run
+for  MI and SSVEP do not have labels associated with trials: these runs could not be used in
+classification tasks.</p></li>
+<li><p><strong>resting_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default False</em><em>)</em>) – if True, return runs corresponding to the resting phases before and after recordings (see paper).</p></li>
+<li><p><strong>sessions</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default</em><em> [</em><em>1</em><em>,</em><em>2</em><em>]</em><em>)</em>) – the list of the sessions to load (2 available).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r74de42efcdd6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E.,
+Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI
+toolbox for three BCI paradigms: An investigation into BCI
+illiteracy. GigaScience, 8(5), 1–16.
+<a class="reference external" href="https://doi.org/10.1093/gigascience/giz002">https://doi.org/10.1093/gigascience/giz002</a></p>
+</div>
+</div>
+</dd></dl>
+
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+
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+  <section id="moabb-datasets-lee2019-ssvep">
+<h1>moabb.datasets.Lee2019_SSVEP<a class="headerlink" href="#moabb-datasets-lee2019-ssvep" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Lee2019_SSVEP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Lee2019_SSVEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resting_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1,</span> <span class="pre">2)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Lee2019.py#L380-L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Lee2019_SSVEP" title="Permalink to this definition">#</a></dt>
+<dd><p>BMI/OpenBMI dataset for SSVEP.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Lee2019_SSVEP</p></td>
+<td><p>54</p></td>
+<td><p>62</p></td>
+<td><p>4</p></td>
+<td><p>50</p></td>
+<td><p>4s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>2</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from Lee et al 2019 <a class="reference internal" href="#rb4e7d9a00d8e-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>EEG signals were recorded with a sampling rate of 1,000 Hz and
+collected with 62 Ag/AgCl electrodes. The EEG amplifier used
+in the experiment was a BrainAmp (Brain Products; Munich,
+Germany). The channels were nasion-referenced and grounded
+to electrode AFz. Additionally, an EMG electrode recorded from
+each flexor digitorum profundus muscle with the olecranon
+used as reference. The EEG/EMG channel configuration and
+indexing numbers are described in Fig. 1. The impedances of the
+EEG electrodes were maintained below 10 k during the entire
+experiment.</p>
+<p>SSVEP paradigm
+Four target SSVEP stimuli were designed to flicker at 5.45, 6.67,
+8.57, and 12 Hz and were presented in four positions (down,
+right, left, and up, respectively) on a monitor. The designed
+paradigm followed the conventional types of SSVEP-based BCI
+systems that require four-direction movements. Partici-
+pants were asked to fixate the center of a black screen and then
+to gaze in the direction where the target stimulus was high-
+lighted in a different color. Each SSVEP stimulus
+was presented for 4 s with an ISI of 6 s. Each target frequency
+was presented 25 times. Therefore, the corrected EEG data had
+100 trials (4 classes x 25 trials) in the offline training phase and
+another 100 trials in the online test phase. Visual feedback was
+presented in the test phase; the estimated target frequency was
+highlighted for 1 s with a red border at the end of each trial.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default True</em><em>)</em>) – if True, return runs corresponding to the training/offline phase (see paper).</p></li>
+<li><p><strong>test_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default: False for MI and SSVEP paradigms</em><em>, </em><em>True for ERP</em><em>)</em>) – if True, return runs corresponding to the test/online phase (see paper). Beware that test_run
+for  MI and SSVEP do not have labels associated with trials: these runs could not be used in
+classification tasks.</p></li>
+<li><p><strong>resting_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default False</em><em>)</em>) – if True, return runs corresponding to the resting phases before and after recordings (see paper).</p></li>
+<li><p><strong>sessions</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default</em><em> [</em><em>1</em><em>,</em><em>2</em><em>]</em><em>)</em>) – the list of the sessions to load (2 available).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb4e7d9a00d8e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E.,
+Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI
+toolbox for three BCI paradigms: An investigation into BCI
+illiteracy. GigaScience, 8(5), 1–16.
+<a class="reference external" href="https://doi.org/10.1093/gigascience/giz002">https://doi.org/10.1093/gigascience/giz002</a></p>
+</div>
+</div>
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
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+  <section id="moabb-datasets-mamem1">
+<h1>moabb.datasets.MAMEM1<a class="headerlink" href="#moabb-datasets-mamem1" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.MAMEM1">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">MAMEM1</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_mamem.py#L170-L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.MAMEM1" title="Permalink to this definition">#</a></dt>
+<dd><p>SSVEP MAMEM 1 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>MAMEM1</p></td>
+<td><p>10</p></td>
+<td><p>256</p></td>
+<td><p>5</p></td>
+<td><p>12-15</p></td>
+<td><p>3s</p></td>
+<td><p>250Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#r69782ab34f79-1" id="id1">[1]</a>.</p>
+<p>EEG signals with 256 channels captured from 11 subjects executing a
+SSVEP-based experimental protocol. Five different frequencies
+(6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual
+stimulation,and the EGI 300 Geodesic EEG System, using a
+stimulation, HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of
+250 Hz has been used for capturing the signals.</p>
+<p>Check the technical report <a class="reference internal" href="#r69782ab34f79-2" id="id2">[2]</a> for more detail.
+From <a class="reference internal" href="#r69782ab34f79-1" id="id3">[1]</a>, subjects were exposed to non-overlapping flickering lights from five
+magenta boxes with frequencies [6.66Hz, 7.5Hz, 8.57Hz 10Hz and 12Hz].
+256 channel EEG recordings were captured.</p>
+<p>Each session of the experimental procedure consisted of the following:</p>
+<ol class="arabic simple">
+<li><p>100 seconds of rest.</p></li>
+<li><p>An adaptation period in which the subject is exposed to eight
+5 second windows of flickering from a magenta box. Each flickering
+window is of a single isolated frequency, randomly chosen from the
+above set, specified in the FREQUENCIES1.txt file under
+‘adaptation’. The individual flickering windows are separated by 5
+seconds of rest.</p></li>
+<li><p>30 seconds of rest.</p></li>
+<li><p>For each of the frequencies from the above set in ascending order,
+also specified in FREQUENCIES1.txt under ‘main trials’:</p>
+<ol class="arabic simple">
+<li><dl class="simple">
+<dt>Three 5 second windows of flickering at the chosen frequency,</dt><dd><p>separated by 5 seconds of rest.</p>
+</dd>
+</dl>
+</li>
+<li><p>30 seconds of rest.</p></li>
+</ol>
+</li>
+</ol>
+<p>This gives a total of 15 flickering windows, or 23 including the
+adaptation period.</p>
+<p>The order of chosen frequencies is the same for each session, although
+there are small-moderate variations in the actual frequencies of each
+individual window. The .freq annotations list the different frequencies at
+a higher level of precision.</p>
+<p><strong>Note</strong>: Each ‘session’ in experiment 1 includes an adaptation period, unlike
+experiment 2 and 3 where each subject undergoes only one adaptation period
+before their first ‘session’.</p>
+<p>From <a class="reference internal" href="#r69782ab34f79-3" id="id4">[3]</a>:</p>
+<p><strong>Eligible signals</strong>: The EEG signal is sensitive to external factors that have
+to do with the environment or the configuration of the acquisition setup
+The research stuff was responsible for the elimination of trials that were
+considered faulty. As a result the following sessions were noted and
+excluded from further analysis:
+1. S003, during session 4 the stimulation program crashed
+2. S004, during session 2 the stimulation program crashed, and
+3. S008, during session 4 the Stim Tracker was detuned.
+Furthermore, we must also note that subject S001 participated in 3 sessions
+and subjects S003 and S004 participated in 4 sessions, compared to all
+other subjects that participated in 5 sessions (NB: in fact, there is only
+3 sessions for subjects 1, 3 and 8, and 4 sessions for subject 4 available
+to download). As a result, the utilized dataset consists of 1104 trials of
+5 seconds each.</p>
+<p><strong>Flickering frequencies</strong>: Usually the refresh rate for an LCD Screen is 60 Hz
+creating a restriction to the number of frequencies that can be selected.
+Specifically, only the frequencies that when divided with the refresh rate
+of the screen result in an integer quotient could be selected. As a result,
+the frequendies that could be obtained were the following: 30.00. 20.00,
+15.00, 1200, 10.00, 857. 7.50 and 6.66 Hz. In addition, it is also
+important to avoid using frequencies that are multiples of another
+frequency, for example making the choice to use 10.00Hz prohibits the use
+of 20.00 and 30.00 Mhz. With the previously described limitations in mind,
+the selected frequencies for the experiment were: 12.00, 10.00, 8.57, 7.50
+and 6.66 Hz.</p>
+<p><strong>Stimuli Layout</strong>: In an effort to keep the experimental process as simple as
+possible, we used only one flickering box instead of more common choices,
+such as 4 or 5 boxes flickering simultaneously The fact that the subject
+could focus on one stimulus without having the distraction of other
+flickering sources allowed us to minimize the noise of our signals and
+verify the appropriateness of our acquisition setup Nevertheless, having
+concluded the optimal configuration for analyzing the EEG signals, the
+experiment will be repeated with more concurrent visual stimulus.</p>
+<p><strong>Trial duration</strong>: The duration of each trial was set to 5 seconds, as this
+time was considered adequate to allow the occipital part of the bran to
+mimic the stimulation frequency and still be small enough for making a
+selection in the context</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r69782ab34f79-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id3">2</a>)</span>
+<p>MAMEM Steady State Visually Evoked Potential EEG Database
+<a class="reference external" href="https://archive.physionet.org/physiobank/database/mssvepdb/">https://archive.physionet.org/physiobank/database/mssvepdb/</a></p>
+</div>
+<div class="citation" id="r69782ab34f79-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>V.P. Oikonomou et al, 2016, Comparative evaluation of state-of-the-art
+algorithms for SSVEP-based BCIs. arXiv.
+<cite>&lt;http://arxiv.org/abs/1602.00904&gt;</cite>-</p>
+</div>
+<div class="citation" id="r69782ab34f79-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">3</a><span class="fn-bracket">]</span></span>
+<p>S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf
+<a class="reference external" href="https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_I_256_channels_11_subjects_5_frequencies_/2068677?file=3793738">https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_I_256_channels_11_subjects_5_frequencies_/2068677?file=3793738</a>  # noqa: E501</p>
+</div>
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+  <section id="moabb-datasets-mamem2">
+<h1>moabb.datasets.MAMEM2<a class="headerlink" href="#moabb-datasets-mamem2" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.MAMEM2">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">MAMEM2</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_mamem.py#L290-L380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.MAMEM2" title="Permalink to this definition">#</a></dt>
+<dd><p>SSVEP MAMEM 2 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>MAMEM2</p></td>
+<td><p>10</p></td>
+<td><p>256</p></td>
+<td><p>5</p></td>
+<td><p>20-30</p></td>
+<td><p>3s</p></td>
+<td><p>250Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#r525ef36ad901-1" id="id1">[1]</a>.</p>
+<p>EEG signals with 256 channels captured from 11 subjects executing a
+SSVEP-based experimental protocol. Five different frequencies
+(6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual
+stimulation,and the EGI 300 Geodesic EEG System, using a
+stimulation, HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of
+250 Hz has been used for capturing the signals.</p>
+<p>Subjects were exposed to flickering lights from five violet boxes with
+frequencies [6.66Hz, 7.5Hz, 8.57Hz, 10Hz, and 12Hz] simultaneously. Prior
+to and during each flicking window, one of the boxes is marked by a yellow
+arrow indicating the box to be focused on by the subject. 256 channel EEG
+recordings were captured.</p>
+<p>From <a class="reference internal" href="#r525ef36ad901-2" id="id2">[2]</a>, each subject underwent a single adaptation period before the first of
+their 5 sessions (unlike experiment 1 in which each session began with its own
+adaptation period). In the adaptation period, the subject is exposed to ten
+5-second flickering windows from the five boxes simultaneously, with the
+target frequencies specified in the FREQUENCIES2.txt file under
+‘adaptation’. The flickering windows are separated by 5 seconds of rest,
+and the 100s adaptation period precedes the first session by 30 seconds.</p>
+<p>Each session consisted of the following:
+For the series of frequencies specified in the FREQUENCIES2.txt file under
+‘sessions’:
+A 5 second window with all boxes flickering and the subject focusing
+on the specified frequency’s marked box, followed by 5 seconds of rest.
+This gives a total of 25 flickering windows for each session (not
+including the first adaptation period). Five minutes of rest before
+the next session (not including the 5th session).</p>
+<p>The order of chosen frequencies is the same for each session, although
+there are small-moderate variations in the actual frequencies of each
+individual window.
+<strong>Note</strong>: Each ‘session’ in experiment 1 includes an adaptation period,
+unlike experiment 2 and 3 where each subject undergoes only one adaptation
+period before their first ‘session’.</p>
+<p><strong>Waveforms and Annotations</strong>
+File names are in the form T0NNn, where NN is the subject number and n is
+a - e for the session letter or x for the adaptation period. Each session
+lasts in the order of several minutes and is sampled at 250Hz. Each session
+and adaptation period has the following files:
+A waveform file of the EEG signals (.dat) along with its header file
+(.hea). If the channel corresponds to an international 10-20 channel then
+it is labeled as such. Otherwise, it is just labeled ‘EEG’. An annotation
+file (.flash) containing the locations of each individual flash. An
+annotation file (.win) containing the locations of the beginning and end
+of each 5 second flickering window. The annotations are labeled as ‘(’ for
+start and ‘)’ for stop, along with auxiliary strings indicating the focal
+frequency of the flashing windows.</p>
+<p>The FREQUENCIES2.txt file indicates the approximate marked frequencies of
+the flickering windows, equal for each session, adaptation, and subject.
+These values are equal to those contained in the .win annotations.</p>
+<p><strong>Observed  artifacts:</strong>
+During the  stimulus  presentation  to  subject  S007  the  research stuff
+noted that the subject had a tendency to eye blink. As a result the
+interference, in matters of artifacts, on the recorded signal is expected
+to be high.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r525ef36ad901-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>MAMEM Steady State Visually Evoked Potential EEG Database
+<a class="reference external" href="https://archive.physionet.org/physiobank/database/mssvepdb/">https://archive.physionet.org/physiobank/database/mssvepdb/</a></p>
+</div>
+<div class="citation" id="r525ef36ad901-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf
+<a class="reference external" href="https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_II_256_channels_11_subjects_5_frequencies_presented_simultaneously_/3153409?file=4911931">https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_II_256_channels_11_subjects_5_frequencies_presented_simultaneously_/3153409?file=4911931</a>  # noqa: E501</p>
+</div>
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+  <section id="moabb-datasets-mamem3">
+<h1>moabb.datasets.MAMEM3<a class="headerlink" href="#moabb-datasets-mamem3" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.MAMEM3">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">MAMEM3</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_mamem.py#L383-L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.MAMEM3" title="Permalink to this definition">#</a></dt>
+<dd><p>SSVEP MAMEM 3 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>MAMEM3</p></td>
+<td><p>10</p></td>
+<td><p>14</p></td>
+<td><p>4</p></td>
+<td><p>20-30</p></td>
+<td><p>3s</p></td>
+<td><p>128Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#r1bc670266ef4-1" id="id1">[1]</a>.</p>
+<p>EEG signals with 14 channels captured from 11 subjects executing a
+SSVEP-based experimental protocol. Five different frequencies
+(6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual
+stimulation, and the Emotiv EPOC, using 14 wireless channels has been used
+for capturing the signals.</p>
+<p>Subjects were exposed to flickering lights from five magenta boxes with
+frequencies [6.66Hz, 7.5Hz, 8.57Hz, 10Hz and 12Hz] simultaneously. Prior
+to and during each flicking window, one of the boxes is marked by a yellow
+arrow indicating the box to be focused on by the subject. The Emotiv EPOC
+14 channel wireless EEG headset was used to capture the subjects’ signals.</p>
+<p>Each subject underwent a single adaptation period before the first of their
+5 sessions (unlike experiment 1 in which each session began with its own
+adaptation period). In the adaptation period, the subject is exposed to ten
+5-second flickering windows from the five boxes simultaneously, with the
+target frequencies specified in the FREQUENCIES3.txt file under
+‘adaptation’. The flickering windows are separated by 5 seconds of rest,
+and the 100s adaptation period precedes the first session by 30 seconds.</p>
+<p>Each session consisted of the following:
+For the series of frequencies specified in the FREQUENCIES3.txt file under
+‘sessions’:
+A 5 second window with all boxes flickering and the subject focusing on
+the specified frequency’s marked box, followed by 5 seconds of rest.
+Between the 12th and 13th flickering window, there is a 30s resting
+period. This gives a total of 25 flickering windows for each session
+(not including the first adaptation period). Five minutes of rest
+before the next session (not including the 5th session).</p>
+<p>The order of chosen frequencies is the same for each session, although
+there are small-moderate variations in the actual frequencies of each
+individual window.</p>
+<p><strong>Note</strong>: Each ‘session’ in experiment 1 includes an adaptation period, unlike
+experiment 2 and 3 where each subject undergoes only one adaptation period
+before their first ‘session’ <a class="reference internal" href="#r1bc670266ef4-2" id="id2">[2]</a>.</p>
+<p><strong>Waveforms and Annotations</strong>
+File names are in the form U0NNn, where NN is the subject number and n is
+a - e for the session letter or x for the adaptation period. In addition,
+session file names end with either i or ii, corresponding to the first 12
+or second 13 windows of the session respectively. Each session lasts in the
+order of several minutes and is sampled at 128Hz.
+Each session half and adaptation period has the following files:
+A waveform file of the EEG signals (.dat) along with its header file
+(.hea). An annotation file (.win) containing the locations of the beginning
+and end of each 5 second flickering window. The annotations are labeled as
+‘(’ for start and ‘)’ for stop, along with auxiliary strings indicating the
+focal frequency of the flashing windows.</p>
+<p>The FREQUENCIES3.txt file indicates the approximate marked frequencies of
+the flickering windows, equal for each session, adaptation, and subject.
+These values are equal to those contained in the .win annotations.</p>
+<p><strong>Trial  manipulation</strong>:
+The  trial  initiation  is  defined by  an  event  code  (32779)  and  the
+end by another (32780). There are five different labels that indicate the
+box subjects were instructed to focus  on  (1, 2, 3, 4 and 5) and
+correspond to frequencies 12.00, 10.00, 8.57, 7.50 and 6.66 Hz respectively.
+5 3 2 1 4 5 2 1 4 3 is the trial sequence for the adaptation and
+4 2 3 5 1 2 5 4 2 3 1 5 4 3 2 4 1 2 5 3 4 1 3 1 3 is the sequence for each
+session.</p>
+<p><strong>Observed  artifacts</strong>:
+During  the  stimulus  presentation to  subject  S007  the  research staff
+noted that the subject had a tendency to eye blink. As a result the
+interference, in matters of artifacts, on the recorded signal is expected
+to be high.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r1bc670266ef4-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>MAMEM Steady State Visually Evoked Potential EEG Database
+<a class="reference external" href="https://archive.physionet.org/physiobank/database/mssvepdb/">https://archive.physionet.org/physiobank/database/mssvepdb/</a></p>
+</div>
+<div class="citation" id="r1bc670266ef4-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf
+<a class="reference external" href="https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_III_14_channels_11_subjects_5_frequencies_presented_simultaneously_/3413851">https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_III_14_channels_11_subjects_5_frequencies_presented_simultaneously_/3413851</a>  # noqa: E501</p>
+</div>
+</div>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-mamem3">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.MAMEM3</span></code><a class="headerlink" href="#examples-using-moabb-datasets-mamem3" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+  <section id="moabb-datasets-nakanishi2015">
+<h1>moabb.datasets.Nakanishi2015<a class="headerlink" href="#moabb-datasets-nakanishi2015" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Nakanishi2015">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Nakanishi2015</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_nakanishi.py#L20-L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Nakanishi2015" title="Permalink to this definition">#</a></dt>
+<dd><p>SSVEP Nakanishi 2015 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Nakanishi2015</p></td>
+<td><p>9</p></td>
+<td><p>8</p></td>
+<td><p>12</p></td>
+<td><p>15</p></td>
+<td><p>4.15s</p></td>
+<td><p>256Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>This dataset contains 12-class joint frequency-phase modulated steady-state
+visual evoked potentials (SSVEPs) acquired from 10 subjects used to
+estimate an online performance of brain-computer interface (BCI) in the
+reference study <a class="reference internal" href="#rfd058abc8888-1" id="id1">[1]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rfd058abc8888-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Masaki Nakanishi, Yijun Wang, Yu-Te Wang and Tzyy-Ping Jung,
+“A Comparison Study of Canonical Correlation Analysis Based Methods for
+Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol.10, no.10,
+e140703, 2015.
+<a class="reference external" href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Nakanishi2015.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_nakanishi.py#L101-L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Nakanishi2015.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Nakanishi2015"><code class="docutils literal notranslate"><span class="pre">Nakanishi2015</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Nakanishi2015.data_path"><code class="docutils literal notranslate"><span class="pre">Nakanishi2015.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-ofner2017">
+<h1>moabb.datasets.Ofner2017<a class="headerlink" href="#moabb-datasets-ofner2017" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Ofner2017">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Ofner2017</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imagined</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">executed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/upper_limb.py#L13-L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Ofner2017" title="Permalink to this definition">#</a></dt>
+<dd><p>Motor Imagery ataset from Ofner et al 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Ofner2017</p></td>
+<td><p>15</p></td>
+<td><p>61</p></td>
+<td><p>7</p></td>
+<td><p>60</p></td>
+<td><p>3s</p></td>
+<td><p>512Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Upper limb Motor imagery dataset from the paper <a class="reference internal" href="#r4e4b953d8efd-1" id="id1">[1]</a>.</p>
+<p><strong>Dataset description</strong></p>
+<p>We recruited 15 healthy subjects aged between 22 and 40 years with a mean
+age of 27 years (standard deviation 5 years). Nine subjects were female,
+and all the subjects except s1 were right-handed.</p>
+<p>We measured each subject in two sessions on two different days, which were
+not separated by more than one week. In the first session the subjects
+performed ME, and MI in the second session. The subjects performed six
+movement types which were the same in both sessions and comprised of
+elbow flexion/extension, forearm supination/pronation and hand open/close;
+all with the right upper limb. All movements started at a
+neutral position: the hand half open, the lower arm extended to 120
+degree and in a neutral rotation, i.e. thumb on the inner side.
+Additionally to the movement classes, a rest class was recorded in which
+subjects were instructed to avoid any movement and to stay in the starting
+position. In the ME session, we instructed subjects to execute sustained
+movements. In the MI session, we asked subjects to perform kinesthetic MI
+of the movements done in the ME session (subjects performed one ME run
+immediately before the MI session to support kinesthetic MI).</p>
+<p>The paradigm was trial-based and cues were displayed on a computer screen
+in front of the subjects, Fig 2 shows the sequence of the paradigm.
+At second 0, a beep sounded and a cross popped up on the computer screen
+(subjects were instructed to fixate their gaze on the cross). Afterwards,
+at second 2, a cue was presented on the computer screen, indicating the
+required task (one out of six movements or rest) to the subjects. At the
+end of the trial, subjects moved back to the starting position. In every
+session, we recorded 10 runs with 42 trials per run. We presented 6
+movement classes and a rest class and recorded 60 trials per class in a
+session.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4e4b953d8efd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Ofner, P., Schwarz, A., Pereira, J. and Müller-Putz, G.R., 2017.
+Upper limb movements can be decoded from the time-domain of
+low-frequency EEG. PloS one, 12(8), p.e0182578.
+<a class="reference external" href="https://doi.org/10.1371/journal.pone.0182578">https://doi.org/10.1371/journal.pone.0182578</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Ofner2017.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">session</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/upper_limb.py#L131-L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Ofner2017.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
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+  <section id="moabb-datasets-physionetmi">
+<h1>moabb.datasets.PhysionetMI<a class="headerlink" href="#moabb-datasets-physionetmi" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.PhysionetMI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">PhysionetMI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imagined</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">executed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/physionet_mi.py#L14-L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.PhysionetMI" title="Permalink to this definition">#</a></dt>
+<dd><p>Physionet Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>PhysionetMI</p></td>
+<td><p>109</p></td>
+<td><p>64</p></td>
+<td><p>4</p></td>
+<td><p>23</p></td>
+<td><p>3s</p></td>
+<td><p>160Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Physionet MI dataset: <a class="reference external" href="https://physionet.org/pn4/eegmmidb/">https://physionet.org/pn4/eegmmidb/</a></p>
+<p>This data set consists of over 1500 one- and two-minute EEG recordings,
+obtained from 109 volunteers <a class="reference internal" href="#r144e24d9faf5-2" id="id1">[2]</a>.</p>
+<p>Subjects performed different motor/imagery tasks while 64-channel EEG were
+recorded using the BCI2000 system (<a class="reference external" href="http://www.bci2000.org">http://www.bci2000.org</a>) <a class="reference internal" href="#r144e24d9faf5-1" id="id2">[1]</a>.
+Each subject performed 14 experimental runs: two one-minute baseline runs
+(one with eyes open, one with eyes closed), and three two-minute runs of
+each of the four following tasks:</p>
+<ol class="arabic simple">
+<li><p>A target appears on either the left or the right side of the screen.
+The subject opens and closes the corresponding fist until the target
+disappears. Then the subject relaxes.</p></li>
+<li><p>A target appears on either the left or the right side of the screen.
+The subject imagines opening and closing the corresponding fist until
+the target disappears. Then the subject relaxes.</p></li>
+<li><p>A target appears on either the top or the bottom of the screen.
+The subject opens and closes either both fists (if the target is on top)
+or both feet (if the target is on the bottom) until the target
+disappears. Then the subject relaxes.</p></li>
+<li><p>A target appears on either the top or the bottom of the screen.
+The subject imagines opening and closing either both fists
+(if the target is on top) or both feet (if the target is on the bottom)
+until the target disappears. Then the subject relaxes.</p></li>
+</ol>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imagined</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default True</em><em>)</em>) – if True, return runs corresponding to motor imagination.</p></li>
+<li><p><strong>executed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default False</em><em>)</em>) – if True, return runs corresponding to motor execution.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r144e24d9faf5-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and
+Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer
+interface (BCI) system. IEEE Transactions on biomedical engineering,
+51(6), pp.1034-1043.</p>
+</div>
+<div class="citation" id="r144e24d9faf5-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">2</a><span class="fn-bracket">]</span></span>
+<p>Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov,
+P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley,
+H.E. and PhysioBank, P., PhysioNet: components of a new research
+resource for complex physiologic signals Circulation 2000 Volume
+101 Issue 23 pp. E215–E220.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.PhysionetMI.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/physionet_mi.py#L153-L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.PhysionetMI.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.PhysionetMI"><code class="docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.PhysionetMI.data_path"><code class="docutils literal notranslate"><span class="pre">PhysionetMI.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-schirrmeister2017">
+<h1>moabb.datasets.Schirrmeister2017<a class="headerlink" href="#moabb-datasets-schirrmeister2017" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Schirrmeister2017">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Schirrmeister2017</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/schirrmeister2017.py#L17-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Schirrmeister2017" title="Permalink to this definition">#</a></dt>
+<dd><p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#re9ae057765a5-1" id="id1">[1]</a></p>
+<p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
+44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
+healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
+roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
+movements divided into 13 runs per subject.  The four classes of movements were
+movements of either the left hand, the right hand, both feet, and rest (no
+movement, but same type of visual cue as for the other classes).  The training
+set consists of the approx.  880 trials of all runs except the last two runs,
+the test set of the approx.  160 trials of the last 2 runs.  This dataset was
+acquired in an EEG lab optimized for non-invasive detection of high- frequency
+movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).</p>
+<p>Depending on the direction of a gray arrow that was shown on black back-
+ground, the subjects had to repetitively clench their toes (downward arrow),
+perform sequential finger-tapping of their left (leftward arrow) or right
+(rightward arrow) hand, or relax (upward arrow).  The movements were selected
+to require little proximal muscular activity while still being complex enough
+to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
+repetitive movements at their own pace, which had to be maintained as long as
+the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
+to 4 s of continuous random inter-trial interval.  The order of presentation
+was pseudo-randomized, with all four arrows being shown every four trials.
+Ideally 13 runs were performed to collect 260 trials of each movement and rest.
+The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
+2004).  The experiment was approved by the ethical committee of the University
+of Freiburg.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="re9ae057765a5-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, Robin Tibor, et al. “Deep learning with convolutional
+neural networks for EEG decoding and visualization.” Human brain mapping 38.11
+(2017): 5391-5420.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Schirrmeister2017.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/schirrmeister2017.py#L77-L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Schirrmeister2017.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Schirrmeister2017"><code class="docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Schirrmeister2017.data_path"><code class="docutils literal notranslate"><span class="pre">Schirrmeister2017.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-shin2017a">
+<h1>moabb.datasets.Shin2017A<a class="headerlink" href="#moabb-datasets-shin2017a" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Shin2017A">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Shin2017A</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">accept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bbci_eeg_fnirs.py#L192-L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Shin2017A" title="Permalink to this definition">#</a></dt>
+<dd><p>Motor Imagey Dataset from Shin et al 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Shin2017A</p></td>
+<td><p>29</p></td>
+<td><p>30</p></td>
+<td><p>2</p></td>
+<td><p>30</p></td>
+<td><p>10s</p></td>
+<td><p>200Hz</p></td>
+<td><p>3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#r678155ec30bb-1" id="id1">[1]</a>.</p>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>You should accept the licence term <a class="reference internal" href="#r678155ec30bb-2" id="id2">[2]</a> to download this dataset, using:
+<code class="docutils literal notranslate"><span class="pre">Shin2017A(accept=True)</span></code></p>
+</div>
+<p><strong>Data Acquisition</strong></p>
+<p>EEG and NIRS data was collected in an ordinary bright room. EEG data was
+recorded by a multichannel BrainAmp EEG amplifier with thirty active
+electrodes (Brain Products GmbH, Gilching, Germany) with linked mastoids
+reference at 1000 Hz sampling rate. The EEG amplifier was also used to
+measure the electrooculogram (EOG), electrocardiogram (ECG) and respiration
+with a piezo based breathing belt. Thirty EEG electrodes were placed on a
+custom-made stretchy fabric cap (EASYCAP GmbH, Herrsching am Ammersee,
+Germany) and placed according to the international 10-5 system (AFp1, AFp2,
+AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7,
+T8, Cz, CCP3h, CCP4h, CCP5h, CCP6h, Pz, P3, P4, P7, P8, PPO1h, PPO2h, POO1,
+POO2 and Fz for ground electrode).</p>
+<p>NIRS data was collected by NIRScout (NIRx GmbH, Berlin, Germany) at 12.5 Hz
+sampling rate. Each adjacent source-detector pair creates one physiological
+NIRS channel. Fourteen sources and sixteen detectors resulting in
+thirty-six
+physiological channels were placed at frontal (nine channels around Fp1,
+Fp2, and Fpz), motor (twelve channels around C3 and C4, respectively) and
+visual areas (three channels around Oz). The inter-optode distance was 30
+mm. NIRS optodes were fixed on the same cap as the EEG electrodes. Ambient
+lights were sufficiently blocked by a firm contact between NIRS optodes and
+scalp and use of an opaque cap.</p>
+<p>EOG was recorded using two vertical (above and below left eye) and two
+horizontal (outer canthus of each eye) electrodes. ECG was recorded based
+on
+Einthoven triangle derivations I and II, and respiration was measured using
+a respiration belt on the lower chest. EOG, ECG and respiration were
+sampled
+at the same sampling rate of the EEG. ECG and respiration data were not
+analyzed in this study, but are provided along with the other signals.</p>
+<p><strong>Experimental Procedure</strong></p>
+<p>The subjects sat on a comfortable armchair in front of a 50-inch white
+screen. The distance between their heads and the screen was 1.6 m. They
+were
+asked not to move any part of the body during the data recording. The
+experiment consisted of three sessions of left and right hand MI (dataset
+A)and MA and baseline tasks (taking a rest without any thought) (dataset B)
+each. Each session comprised a 1 min pre-experiment resting period, 20
+repetitions of the given task and a 1 min post-experiment resting
+period. The task started with 2 s of a visual introduction of the task,
+followed by 10 s of a task period and resting period which was given
+randomly from 15 to 17 s. At the beginning and end of the task period, a
+short beep (250 ms) was played. All instructions were displayed on the
+white
+screen by a video projector. MI and MA tasks were performed in separate
+sessions but in alternating order (i.e., sessions 1, 3 and 5 for MI
+(dataset
+A) and sessions 2, 4 and 6 for MA (dataset B)). Fig. 2 shows the schematic
+diagram of the experimental paradigm. Five sorts of motion artifacts
+induced
+by eye and head movements (dataset C) were measured. The motion artifacts
+were recorded after all MI and MA task recordings. The experiment did not
+include the pre- and post-experiment resting state periods.</p>
+<p><strong>Motor Imagery (Dataset A)</strong></p>
+<p>For motor imagery, subjects were instructed to perform haptic motor imagery
+(i.e. to imagine the feeling of opening and closing their hands as they
+were
+grabbing a ball) to ensure that actual motor imagery, not visual imagery,
+was performed. All subjects were naive to the MI experiment. For the visual
+instruction, a black arrow pointing to either the left or right side
+appeared at the center of the screen for 2 s. The arrow disappeared with a
+short beep sound and then a black fixation cross was displayed during the
+task period. The subjects were asked to imagine hand gripping (opening and
+closing their hands) in a 1 Hz pace. This pace was shown to and repeated by
+the subjects by performing real hand gripping before the experiment. Motor
+imagery was performed continuously over the task period. The task period
+was finished with a short beep sound and a ‘STOP’ displayed for 1s on the
+screen. The fixation cross was displayed again during the rest period and
+the subjects were asked to gaze at it to minimize their eye movements. This
+process was repeated twenty times in a single session (ten trials per
+condition in a single session; thirty trials in the whole sessions). In a
+single session, motor imagery tasks were performed on the basis of ten
+subsequent blocks randomly consisting of one of two conditions: Either
+first left and then right hand motor imagery or vice versa.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r678155ec30bb-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Shin, J., von Lühmann, A., Blankertz, B., Kim, D.W., Jeong, J.,
+Hwang, H.J. and Müller, K.R., 2017. Open access dataset for EEG+NIRS
+single-trial classification. IEEE Transactions on Neural Systems
+and Rehabilitation Engineering, 25(10), pp.1735-1745.</p>
+</div>
+<div class="citation" id="r678155ec30bb-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>GNU General Public License, Version 3
+<a class="reference external" href="https://www.gnu.org/licenses/gpl-3.0.txt">https://www.gnu.org/licenses/gpl-3.0.txt</a></p>
+</div>
+</div>
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Shin2017A"><code class="docutils literal notranslate"><span class="pre">Shin2017A</span></code></a></li>
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+  <section id="moabb-datasets-shin2017b">
+<h1>moabb.datasets.Shin2017B<a class="headerlink" href="#moabb-datasets-shin2017b" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Shin2017B">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Shin2017B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">accept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bbci_eeg_fnirs.py#L315-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Shin2017B" title="Permalink to this definition">#</a></dt>
+<dd><p>Mental Arithmetic Dataset from Shin et al 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Shin2017B</p></td>
+<td><p>29</p></td>
+<td><p>30</p></td>
+<td><p>2</p></td>
+<td><p>30</p></td>
+<td><p>10s</p></td>
+<td><p>200Hz</p></td>
+<td><p>3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#r2f4254a26ae2-1" id="id1">[1]</a>.</p>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>You should accept the licence term <a class="reference internal" href="#r2f4254a26ae2-2" id="id2">[2]</a> to download this dataset, using:
+<code class="docutils literal notranslate"><span class="pre">Shin2017B(accept=True)</span></code></p>
+</div>
+<p><strong>Data Acquisition</strong></p>
+<p>EEG and NIRS data was collected in an ordinary bright room. EEG data was
+recorded by a multichannel BrainAmp EEG amplifier with thirty active
+electrodes (Brain Products GmbH, Gilching, Germany) with linked mastoids
+reference at 1000 Hz sampling rate. The EEG amplifier was also used to
+measure the electrooculogram (EOG), electrocardiogram (ECG) and respiration
+with a piezo based breathing belt. Thirty EEG electrodes were placed on a
+custom-made stretchy fabric cap (EASYCAP GmbH, Herrsching am Ammersee,
+Germany) and placed according to the international 10-5 system (AFp1, AFp2,
+AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7,
+T8, Cz, CCP3h, CCP4h, CCP5h, CCP6h, Pz, P3, P4, P7, P8, PPO1h, PPO2h, POO1,
+POO2 and Fz for ground electrode).</p>
+<p>NIRS data was collected by NIRScout (NIRx GmbH, Berlin, Germany) at 12.5 Hz
+sampling rate. Each adjacent source-detector pair creates one physiological
+NIRS channel. Fourteen sources and sixteen detectors resulting in
+thirty-six
+physiological channels were placed at frontal (nine channels around Fp1,
+Fp2, and Fpz), motor (twelve channels around C3 and C4, respectively) and
+visual areas (three channels around Oz). The inter-optode distance was 30
+mm. NIRS optodes were fixed on the same cap as the EEG electrodes. Ambient
+lights were sufficiently blocked by a firm contact between NIRS optodes and
+scalp and use of an opaque cap.</p>
+<p>EOG was recorded using two vertical (above and below left eye) and two
+horizontal (outer canthus of each eye) electrodes. ECG was recorded based
+on
+Einthoven triangle derivations I and II, and respiration was measured using
+a respiration belt on the lower chest. EOG, ECG and respiration were
+sampled
+at the same sampling rate of the EEG. ECG and respiration data were not
+analyzed in this study, but are provided along with the other signals.</p>
+<p><strong>Experimental Procedure</strong></p>
+<p>The subjects sat on a comfortable armchair in front of a 50-inch white
+screen. The distance between their heads and the screen was 1.6 m. They
+were
+asked not to move any part of the body during the data recording. The
+experiment consisted of three sessions of left and right hand MI (dataset
+A)and MA and baseline tasks (taking a rest without any thought) (dataset B)
+each. Each session comprised a 1 min pre-experiment resting period, 20
+repetitions of the given task and a 1 min post-experiment resting
+period. The task started with 2 s of a visual introduction of the task,
+followed by 10 s of a task period and resting period which was given
+randomly from 15 to 17 s. At the beginning and end of the task period, a
+short beep (250 ms) was played. All instructions were displayed on the
+white
+screen by a video projector. MI and MA tasks were performed in separate
+sessions but in alternating order (i.e., sessions 1, 3 and 5 for MI
+(dataset
+A) and sessions 2, 4 and 6 for MA (dataset B)). Fig. 2 shows the schematic
+diagram of the experimental paradigm. Five sorts of motion artifacts
+induced
+by eye and head movements (dataset C) were measured. The motion artifacts
+were recorded after all MI and MA task recordings. The experiment did not
+include the pre- and post-experiment resting state periods.</p>
+<p><strong>Mental Arithmetic (Dataset B)</strong></p>
+<p>For the visual instruction of the MA task, an initial subtraction such as
+‘three-digit number minus one-digit number’ (e.g., 384-8) appeared at the
+center of the screen for 2 s. The subjects were instructed to memorize the
+numbers while the initial subtraction was displayed on the screen. The
+initial subtraction disappeared with a short beep sound and a black
+fixation cross was displayed during the task period in which the subjects
+were asked
+to repeatedly perform to subtract the one-digit number from the result of
+the previous subtraction. For the baseline task, no specific sign but the
+black fixation cross was displayed on the screen, and the subjects were
+instructed to take a rest. Note that there were other rest periods between
+the MA and baseline task periods, as same with the MI paradigm. Both task
+periods were finished with a short beep sound and a ‘STOP’ displayed for
+1 s on the screen. The fixation cross was displayed again during the rest
+period. MA and baseline trials were randomized in the same way as MI.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2f4254a26ae2-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Shin, J., von Lühmann, A., Blankertz, B., Kim, D.W., Jeong, J.,
+Hwang, H.J. and Müller, K.R., 2017. Open access dataset for EEG+NIRS
+single-trial classification. IEEE Transactions on Neural Systems
+and Rehabilitation Engineering, 25(10), pp.1735-1745.</p>
+</div>
+<div class="citation" id="r2f4254a26ae2-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>GNU General Public License, Version 3
+<a class="reference external" href="https://www.gnu.org/licenses/gpl-3.0.txt">https://www.gnu.org/licenses/gpl-3.0.txt</a></p>
+</div>
+</div>
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
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+  <section id="moabb-datasets-sosulski2019">
+<h1>moabb.datasets.Sosulski2019<a class="headerlink" href="#moabb-datasets-sosulski2019" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Sosulski2019">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Sosulski2019</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_soas_as_sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_soa_60</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject_non_iid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/sosulski2019.py#L17-L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Sosulski2019" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 dataset from initial spot study.</p>
+<p>Dataset <a class="reference internal" href="#rc783cf247497-1" id="id1">[1]</a>, study on spatial transfer between SOAs <a class="reference internal" href="#rc783cf247497-2" id="id2">[2]</a>, actual paradigm / online optimization <a class="reference internal" href="#rc783cf247497-3" id="id3">[3]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Sosulski2019</p></td>
+<td><p>13</p></td>
+<td><p>31</p></td>
+<td><p>7500 NT / 1500 T</p></td>
+<td><p>1.2s</p></td>
+<td><p>1000Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong>
+This dataset contains multiple small trials of an auditory oddball paradigm. The paradigm presented two different
+sinusoidal tones. A low-pitched (500 Hz, 40 ms duration) non-target tone and a high-pitched (1000 Hz,
+40 ms duration) target tone. Subjects were instructed to attend to the high-pitched target tones and ignore the
+low-pitched tones.</p>
+<p>One trial (= one file) consisted of 90 tones, 15 targets and 75 non-targets. The order was pseudo-randomized in a
+way that at least two non-target tones occur between two target tones. Additionally, if you split the 90 tones of
+one trial into consecutive sets of six tones, there will always be exactly one target and five non-target tones
+in each set.</p>
+<p>In the first part of the experiment (run 1), each subject performed 50-70 trials with various different stimulus
+onset asynchronies (SOAs) – i.e. the time between the onset of successive tones – for each trial. In the second
+part (run 2), 4-5 SOAs were played, with blocks of 5 trials having the same SOA. All SOAs were in the range of 60
+ms to 600 ms. Regardless of the experiment part, after a set of five trials, subjects were given the opportunity
+to take a short break to e.g. drink etc.</p>
+<p>Finally, before and after each run, resting data was recorded. One minute with eyes open and one minute with eyes
+closed, i.e. in total four minutes of resting data are available for each subject.</p>
+<p>Data was recorded using a BrainAmp DC (BrainVision) amplifier and a 31 passive electrode EasyCap. The cap was
+placed according to the extended 10-20 electrode layout. The reference electrode was placed on the nose. Before
+recording, the cap was prepared such that impedances on all electrodes were around 20 kOhm. The EEG signal was
+recorded at 1000 Hz.</p>
+<p>The data contains 31 scalp channels, one EOG channel and five miscellaneous non-EEG signal channels. However,
+only scalp EEG and the EOG channel is available in all subjects. The markers in the marker file indicate the
+onset of target tones (21) and non-target tones (1).</p>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Note that this wrapper currently only loads the second part of the experiment and uses pseudo-sessions
+to achieve the functionality to handle different conditions in MOABB. As a result, the statistical testing
+features of MOABB cannot be used for this dataset.</p>
+</div>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc783cf247497-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Sosulski, J., Tangermann, M.: Electroencephalogram signals recorded from 13 healthy subjects during an
+auditory oddball paradigm under different stimulus onset asynchrony conditions.
+Dataset. DOI: 10.6094/UNIFR/154576</p>
+</div>
+<div class="citation" id="rc783cf247497-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Sosulski, J., Tangermann, M.: Spatial filters for auditory evoked potentials transfer between different
+experimental conditions. Graz BCI Conference. 2019.</p>
+</div>
+<div class="citation" id="rc783cf247497-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Sosulski, J., Hübner, D., Klein, A., Tangermann, M.:  Online Optimization of Stimulation Speed in
+an Auditory Brain-Computer Interface under Time Constraints. arXiv preprint. 2021.</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.5.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Sosulski2019.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/sosulski2019.py#L175-L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Sosulski2019.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Sosulski2019"><code class="docutils literal notranslate"><span class="pre">Sosulski2019</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Sosulski2019.data_path"><code class="docutils literal notranslate"><span class="pre">Sosulski2019.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-thielen2015">
+<h1>moabb.datasets.Thielen2015<a class="headerlink" href="#moabb-datasets-thielen2015" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Thielen2015">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Thielen2015</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/thielen2015.py#L22-L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Thielen2015" title="Permalink to this definition">#</a></dt>
+<dd><p>c-VEP dataset from Thielen et al. (2015)</p>
+<p>Dataset <a class="reference internal" href="#r88057a8367d2-1" id="id1">[1]</a> from the study on reconvolution for c-VEP <a class="reference internal" href="#r88057a8367d2-2" id="id2">[2]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Sessions</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>#Trial classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>#Epoch classes</p></th>
+<th class="head"><p>#Epochs / class</p></th>
+<th class="head"><p>Codes</p></th>
+<th class="head"><p>Presentation rate</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Thielen2015</p></td>
+<td><p>12</p></td>
+<td><p>1</p></td>
+<td><p>2048Hz</p></td>
+<td><p>64</p></td>
+<td><p>4.2s</p></td>
+<td><p>36</p></td>
+<td><p>3</p></td>
+<td><p>2</p></td>
+<td><p>27216 NT / 27216 T</p></td>
+<td><p>Gold codes</p></td>
+<td><p>120Hz</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>EEG recordings were obtained with a sampling rate of 2048 Hz, using a setup comprising 64 Ag/AgCl electrodes, and
+amplified by a Biosemi ActiveTwo EEG amplifier. Electrode placement followed the international 10-10 system.</p>
+<p>During the experimental sessions, participants actively operated a 6 x 6 visual speller brain-computer interface
+(BCI) with real-time feedback, encompassing 36 distinct classes. Each cell within the symbol grid underwent
+luminance modulation at full contrast, achieved through the application of pseudo-random noise-codes derived from a
+set of modulated Gold codes. These binary codes have a balanced distribution of ones and zeros while adhering to a
+limited run-length pattern, with a maximum run-length of 2 bits. Codes were presented at a rate of 120 Hz. Given
+that one cycle of these modulated Gold codes comprises 126 bits, the duration of a complete cycle spans 1.05
+seconds.</p>
+<p>Throughout the experiment, participants underwent four distinct blocks: an initial practice block consisting of two
+runs, followed by a training block of one run. Subsequently, they engaged in a copy-spelling block comprising six
+runs, and finally, a free-spelling block consisting of one run. Between the training and copy-spelling block, a
+classifier was calibrated using data from the training block. This calibrated classifier was then applied during
+both the copy-spelling and free-spelling runs. Additionally, during calibration, the stimulation codes were
+tailored and optimized specifically for each individual participant.</p>
+<p>Among the six copy-spelling runs, there were three fixed-length runs. Trials in these runs started with a cueing
+phase, where the target symbol was highlighted in a green hue for 1 second. Participants maintained their gaze
+fixated on the target symbol as all symbols flashed in sync with their corresponding pseudo-random noise-codes for a
+duration of 4.2 seconds (equivalent to 4 code cycles). Immediately following this stimulation, the output of the
+classifier was shown by coloring the cell blue for 1 second. Each run consisted of 36 trials, presented in a
+randomized order.</p>
+<p>Here, our focus is solely on the three copy-spelling runs characterized by fixed-length trials lasting 4.2 seconds
+(equivalent to four code cycles). The other three runs utilized a dynamic stopping procedure, resulting in trials of
+varying durations, rendering them unsuitable for benchmarking purposes. Similarly, the practice and free-spelling
+runs included dynamic stopping and are ignored in this dataset. The training dataset, comprising 36 trials, used a
+different noise-code set, and is therefore also ignored in this dataset. In total, this dataset should contain 108
+trials of 4.2 seconds each, with 3 repetitions for each of the 36 codes.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r88057a8367d2-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Thielen, J. (Jordy), Jason Farquhar, Desain, P.W.M. (Peter) (2023): Broad-Band Visually Evoked Potentials:
+Re(con)volution in Brain-Computer Interfacing. Version 2. Radboud University. (dataset).
+DOI: <a class="reference external" href="https://doi.org/10.34973/1ecz-1232">https://doi.org/10.34973/1ecz-1232</a></p>
+</div>
+<div class="citation" id="r88057a8367d2-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Thielen, J., Van Den Broek, P., Farquhar, J., &amp; Desain, P. (2015). Broad-Band visually evoked potentials:
+re(con)volution in brain-computer interfacing. PLOS ONE, 10(7), e0133797.
+DOI: <a class="reference external" href="https://doi.org/10.1371/journal.pone.0133797">https://doi.org/10.1371/journal.pone.0133797</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.0.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Thielen2015.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/thielen2015.py#L161-L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Thielen2015.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the data paths of a single subject.</p>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Thielen2015"><code class="docutils literal notranslate"><span class="pre">Thielen2015</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Thielen2015.data_path"><code class="docutils literal notranslate"><span class="pre">Thielen2015.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-thielen2021">
+<h1>moabb.datasets.Thielen2021<a class="headerlink" href="#moabb-datasets-thielen2021" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Thielen2021">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Thielen2021</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/thielen2021.py#L72-L235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Thielen2021" title="Permalink to this definition">#</a></dt>
+<dd><p>c-VEP dataset from Thielen et al. (2021)</p>
+<p>Dataset <a class="reference internal" href="#r2fb52eacc4cb-1" id="id1">[1]</a> from the study on zero-training c-VEP <a class="reference internal" href="#r2fb52eacc4cb-2" id="id2">[2]</a>.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Sessions</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>#Trial classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>#Epoch classes</p></th>
+<th class="head"><p>#Epochs / class</p></th>
+<th class="head"><p>Codes</p></th>
+<th class="head"><p>Presentation rate</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Thielen2021</p></td>
+<td><p>30</p></td>
+<td><p>1</p></td>
+<td><p>512Hz</p></td>
+<td><p>8</p></td>
+<td><p>31.5s</p></td>
+<td><p>20</p></td>
+<td><p>5</p></td>
+<td><p>2</p></td>
+<td><p>94500 NT / 94500 T</p></td>
+<td><p>Gold codes</p></td>
+<td><p>60Hz</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p><strong>Dataset description</strong></p>
+<p>EEG recordings were acquired at a sampling rate of 512 Hz, employing 8 Ag/AgCl electrodes. The Biosemi ActiveTwo EEG
+amplifier was utilized during the experiment. The electrode array consisted of Fz, T7, O1, POz, Oz, Iz, O2, and T8,
+connected as EXG channels. This is a custom electrode montage as optimized in a previous study for c-VEP, see <a class="reference internal" href="#r2fb52eacc4cb-3" id="id3">[3]</a>.</p>
+<p>During the experimental sessions, participants engaged in passive operation (i.e., without feedback) of a 4 x 5
+visual speller brain-computer interface (BCI) comprising 20 distinct classes. Each cell of the symbol grid
+underwent luminance modulation at full contrast, accomplished through pseudo-random noise-codes derived from a
+collection of modulated Gold codes. These codes are binary, have a balanced distribution of ones and zeros, and
+adhere to a limited run-length pattern (maximum run-length of 2 bits). The codes were presented at a presentation
+rate of 60 Hz. As one cycle of these modulated Gold codes contains 126 bits, the duration of one cycle is 2.1
+seconds.</p>
+<p>For each of the five blocks, a trial started with a cueing phase, during which the target symbol was highlighted in
+a green hue for a duration of 1 second. Following this, participants maintained their gaze fixated on the target
+symbol while all symbols flashed in accordance with their respective pseudo-random noise-codes for a duration of
+31.5 seconds (i.e., 15 code cycles). Each block encompassed 20 trials, presented in a randomized sequence, thereby
+ensuring that each symbol was attended to once within the span of a block.</p>
+<p>Note, here, we only load the offline data of this study and ignore the online phase.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2fb52eacc4cb-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Thielen, J. (Jordy), Pieter Marsman, Jason Farquhar, Desain, P.W.M. (Peter) (2023): From full calibration to
+zero training for a code-modulated visual evoked potentials brain computer interface. Version 3. Radboud
+University. (dataset).
+DOI: <a class="reference external" href="https://doi.org/10.34973/9txv-z787">https://doi.org/10.34973/9txv-z787</a></p>
+</div>
+<div class="citation" id="r2fb52eacc4cb-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Thielen, J., Marsman, P., Farquhar, J., &amp; Desain, P. (2021). From full calibration to zero training for a
+code-modulated visual evoked potentials for brain–computer interface. Journal of Neural Engineering, 18(5),
+056007.
+DOI: <a class="reference external" href="https://doi.org/10.1088/1741-2552/abecef">https://doi.org/10.1088/1741-2552/abecef</a></p>
+</div>
+<div class="citation" id="r2fb52eacc4cb-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Ahmadi, S., Borhanazad, M., Tump, D., Farquhar, J., &amp; Desain, P. (2019). Low channel count montages using
+sensor tying for VEP-based BCI. Journal of Neural Engineering, 16(6), 066038.
+DOI: <a class="reference external" href="https://doi.org/10.1088/1741-2552/ab4057">https://doi.org/10.1088/1741-2552/ab4057</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.6.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Thielen2021.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/thielen2021.py#L206-L235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Thielen2021.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the data paths of a single subject.</p>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Thielen2021"><code class="docutils literal notranslate"><span class="pre">Thielen2021</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Thielen2021.data_path"><code class="docutils literal notranslate"><span class="pre">Thielen2021.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-wang2016">
+<h1>moabb.datasets.Wang2016<a class="headerlink" href="#moabb-datasets-wang2016" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Wang2016">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Wang2016</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_wang.py#L24-L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Wang2016" title="Permalink to this definition">#</a></dt>
+<dd><p>SSVEP Wang 2016 dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials length</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Wang2016</p></td>
+<td><p>34</p></td>
+<td><p>62</p></td>
+<td><p>40</p></td>
+<td><p>6</p></td>
+<td><p>5s</p></td>
+<td><p>250Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#rd6745bf218a6-1" id="id1">[1]</a>.</p>
+<p>This dataset gathered SSVEP-BCI recordings of 35 healthy subjects (17
+females, aged 17-34 years, mean age: 22 years) focusing on 40 characters
+flickering at different frequencies (8-15.8 Hz with an interval of 0.2 Hz).
+For each subject, the experiment consisted of 6 blocks. Each block
+contained 40 trials corresponding to all 40 characters indicated in a
+random order. Each trial started with a visual cue (a red square)
+indicating a target stimulus. The cue appeared for 0.5 s on the screen.
+Subjects were asked to shift their gaze to the target as soon as possible
+within the cue duration. Following the cue offset, all stimuli started to
+flicker on the screen concurrently and lasted 5 s. After stimulus offset,
+the screen was blank for 0.5 s before the next trial began, which allowed
+the subjects to have short breaks between consecutive trials. Each trial
+lasted a total of 6 s. To facilitate visual fixation, a red triangle
+appeared below the flickering target during the stimulation period. In each
+block, subjects were asked to avoid eye blinks during the stimulation
+period. To avoid visual fatigue, there was a rest for several minutes
+between two consecutive blocks.</p>
+<p>EEG data were acquired using a Synamps2 system (Neuroscan, Inc.) with a
+sampling rate of 1000 Hz. The amplifier frequency passband ranged from
+0.15 Hz to 200 Hz. Sixty-four channels covered the whole scalp of the
+subject and were aligned according to the international 10-20 system.
+The ground was placed on midway between Fz and FPz. The reference was
+located on the vertex. Electrode impedances were kept below 10 KΩ. To
+remove the common power-line noise, a notch filter at 50 Hz was applied
+in data recording. Event triggers generated by the computer to the
+amplifier and recorded on an event channel synchronized to the EEG data.</p>
+<p>The continuous EEG data was segmented into 6 s epochs (500 ms pre-stimulus,
+5.5 s post-stimulus onset). The epochs were subsequently downsampled to
+250 Hz. Thus each trial consisted of 1500 time points. Finally, these data
+were stored as double-precision floating-point values in MATLAB and were
+named as subject indices (i.e., S01.mat, …, S35.mat). For each file, the
+data loaded in MATLAB generate a 4-D matrix named ‘data’ with dimensions
+of [64, 1500, 40, 6]. The four dimensions indicate ‘Electrode index’,
+‘Time points’, ‘Target index’, and ‘Block index’. The electrode positions
+were saved in a ‘64-channels.loc’ file. Six trials were available for each
+SSVEP frequency. Frequency and phase values for the 40 target indices were
+saved in a ‘Freq_Phase.mat’ file.</p>
+<p>Information for all subjects was listed in a ‘Sub_info.txt’ file. For each
+subject, there are five factors including ‘Subject Index’, ‘Gender’, ‘Age’,
+‘Handedness’, and ‘Group’. Subjects were divided into an ‘experienced’
+group (eight subjects, S01-S08) and a ‘naive’ group (27 subjects, S09-S35)
+according to their experience in SSVEP-based BCIs.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd6745bf218a6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Y. Wang, X. Chen, X. Gao and S. Gao, 2017, “A Benchmark Dataset for
+SSVEP-Based Brain–Computer Interfaces,” in IEEE Transactions on Neural
+Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1746-1752,
+doi: 10.1109/TNSRE.2016.2627556.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Wang2016.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/ssvep_wang.py#L155-L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Wang2016.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
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+  <section id="moabb-datasets-weibo2014">
+<h1>moabb.datasets.Weibo2014<a class="headerlink" href="#moabb-datasets-weibo2014" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Weibo2014">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Weibo2014</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Weibo2014.py#L64-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Weibo2014" title="Permalink to this definition">#</a></dt>
+<dd><p>Motor Imagery dataset from Weibo et al 2014.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Weibo2014</p></td>
+<td><p>10</p></td>
+<td><p>60</p></td>
+<td><p>7</p></td>
+<td><p>80</p></td>
+<td><p>4s</p></td>
+<td><p>200Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from the article <em>Evaluation of EEG oscillatory patterns and
+cognitive process during simple and compound limb motor imagery</em> <a class="reference internal" href="#rdba38df6f4f0-1" id="id1">[1]</a>.</p>
+<p>It contains data recorded on 10 subjects, with 60 electrodes.</p>
+<p>This dataset was used to investigate the differences of the EEG patterns
+between simple limb motor imagery and compound limb motor
+imagery. Seven kinds of mental tasks have been designed, involving three
+tasks of simple limb motor imagery (left hand, right hand, feet), three
+tasks of compound limb motor imagery combining hand with hand/foot
+(both hands, left hand combined with right foot, right hand combined with
+left foot) and rest state.</p>
+<p>At the beginning of each trial (8 seconds), a white circle appeared at the
+center of the monitor. After 2 seconds, a red circle (preparation cue)
+appeared for 1 second to remind the subjects of paying attention to the
+character indication next. Then red circle disappeared and character
+indication (‘Left Hand’, ‘Left Hand &amp; Right Foot’, et al) was presented on
+the screen for 4 seconds, during which the participants were asked to
+perform kinesthetic motor imagery rather than a visual type of imagery
+while avoiding any muscle movement. After 7 seconds, ‘Rest’ was presented
+for 1 second before next trial (Fig. 1(a)). The experiments were divided
+into 9 sections, involving 8 sections consisting of 60 trials each for six
+kinds of MI tasks (10 trials for each MI task in one section) and one
+section consisting of 80 trials for rest state. The sequence of six MI
+tasks was randomized. Intersection break was about 5 to 10 minutes.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rdba38df6f4f0-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Yi, Weibo, et al. “Evaluation of EEG oscillatory patterns and
+cognitive process during simple and compound limb motor imagery.”
+PloS one 9.12 (2014). <a class="reference external" href="https://doi.org/10.1371/journal.pone.0114853">https://doi.org/10.1371/journal.pone.0114853</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Weibo2014.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Weibo2014.py#L179-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Weibo2014.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
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+<h1>moabb.datasets.Zhou2016<a class="headerlink" href="#moabb-datasets-zhou2016" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.Zhou2016">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.</span></span><span class="sig-name descname"><span class="pre">Zhou2016</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Zhou2016.py#L50-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Zhou2016" title="Permalink to this definition">#</a></dt>
+<dd><p>Motor Imagery dataset from Zhou et al 2016.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Zhou2016</p></td>
+<td><p>4</p></td>
+<td><p>14</p></td>
+<td><p>3</p></td>
+<td><p>160</p></td>
+<td><p>5s</p></td>
+<td><p>250Hz</p></td>
+<td><p>3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from the article <em>A Fully Automated Trial Selection Method for
+Optimization of Motor Imagery Based Brain-Computer Interface</em> <a class="reference internal" href="#r772135428493-1" id="id1">[1]</a>.
+This dataset contains data recorded on 4 subjects performing 3 type of
+motor imagery: left hand, right hand and feet.</p>
+<p>Every subject went through three sessions, each of which contained two
+consecutive runs with several minutes inter-run breaks, and each run
+comprised 75 trials (25 trials per class). The intervals between two
+sessions varied from several days to several months.</p>
+<p>A trial started by a short beep indicating 1 s preparation time,
+and followed by a red arrow pointing randomly to three directions (left,
+right, or bottom) lasting for 5 s and then presented a black screen for
+4 s. The subject was instructed to immediately perform the imagination
+tasks of the left hand, right hand or foot movement respectively according
+to the cue direction, and try to relax during the black screen.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r772135428493-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Zhou B, Wu X, Lv Z, Zhang L, Guo X (2016) A Fully Automated
+Trial Selection Method for Optimization of Motor Imagery Based
+Brain-Computer Interface. PLoS ONE 11(9).
+<a class="reference external" href="https://doi.org/10.1371/journal.pone.0162657">https://doi.org/10.1371/journal.pone.0162657</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.Zhou2016.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/Zhou2016.py#L121-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.Zhou2016.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-zhou2016">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.Zhou2016</span></code><a class="headerlink" href="#examples-using-moabb-datasets-zhou2016" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to track the CO2 footprint using `CodeCarbon library &lt;https..."><img alt="Benchmarking with MOABB showing the CO2 footprint" src="../_images/sphx_glr_example_codecarbon_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB showing the CO2 footprint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.Zhou2016.data_path"><code class="docutils literal notranslate"><span class="pre">Zhou2016.data_path()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-datasets-zhou2016">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.Zhou2016</span></code></a></li>
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+  <section id="moabb-datasets-base-basedataset">
+<h1>moabb.datasets.base.BaseDataset<a class="headerlink" href="#moabb-datasets-base-basedataset" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.base.BaseDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.base.</span></span><span class="sig-name descname"><span class="pre">BaseDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subjects</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sessions_per_subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">events</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">paradigm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">doi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/base.py#L181-L566"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.base.BaseDataset" title="Permalink to this definition">#</a></dt>
+<dd><p>Abstract Moabb BaseDataset.</p>
+<p>Parameters required for all datasets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subjects</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of subject number (or tuple or numpy array)</p></li>
+<li><p><strong>sessions_per_subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sessions per subject (if varying, take minimum)</p></li>
+<li><p><strong>events</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> of </em><em>strings</em>) – String codes for events matched with labels in the stim channel.
+Currently imagery codes codes can include:
+- left_hand
+- right_hand
+- hands
+- feet
+- rest
+- left_hand_right_foot
+- right_hand_left_foot
+- tongue
+- navigation
+- subtraction
+- word_ass (for word association)</p></li>
+<li><p><strong>code</strong> (<em>string</em>) – Unique identifier for dataset, used in all plots.
+The code should be in CamelCase.</p></li>
+<li><p><strong>interval</strong> (<em>list with 2 entries</em>) – Imagery interval as defined in the dataset description</p></li>
+<li><p><strong>paradigm</strong> (<em>[</em><em>'p300'</em><em>,</em><em>'imagery'</em><em>, </em><em>'ssvep'</em><em>]</em>) – Defines what sort of dataset this is</p></li>
+<li><p><strong>doi</strong> (<em>DOI for dataset</em><em>, </em><em>optional</em><em> (</em><em>for now</em><em>)</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.base.BaseDataset.data_path">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/base.py#L534-L566"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.base.BaseDataset.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.base.BaseDataset.download">
+<span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/base.py#L353-L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.base.BaseDataset.download" title="Permalink to this definition">#</a></dt>
+<dd><p>Download all data from the dataset.</p>
+<p>This function is only useful to download all the dataset at once.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject_list</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – List of subjects id to download, if None all subjects
+are downloaded.</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><em>None</em>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>accept</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Accept licence term to download the data, if any. Default: False</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.base.BaseDataset.get_data">
+<span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subjects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/base.py#L275-L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.base.BaseDataset.get_data" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the data corresponding to a list of subjects.</p>
+<p>The returned data is a dictionary with the following structure:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;subject_id&#39;</span> <span class="p">:</span>
+            <span class="p">{</span><span class="s1">&#39;session_id&#39;</span><span class="p">:</span>
+                <span class="p">{</span><span class="s1">&#39;run_id&#39;</span><span class="p">:</span> <span class="n">run</span><span class="p">}</span>
+            <span class="p">}</span>
+        <span class="p">}</span>
+</pre></div>
+</div>
+<p>subjects are on top, then we have sessions, then runs.
+A sessions is a recording done in a single day, without removing the
+EEG cap. A session is constitued of at least one run. A run is a single
+contiguous recording. Some dataset break session in multiple runs.</p>
+<p>Processing steps can optionally be applied to the data using the
+<code class="docutils literal notranslate"><span class="pre">*_pipeline</span></code> arguments. These pipelines are applied in the
+following order: <code class="docutils literal notranslate"><span class="pre">raw_pipeline</span></code> -&gt; <code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code> -&gt;
+<code class="docutils literal notranslate"><span class="pre">array_pipeline</span></code>. If a <code class="docutils literal notranslate"><span class="pre">*_pipeline</span></code> argument is <code class="docutils literal notranslate"><span class="pre">None</span></code>,
+the step will be skipped. Therefore, the <code class="docutils literal notranslate"><span class="pre">array_pipeline</span></code> may
+either receive a <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.io.Raw</span></code></a> or a <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a> object
+as input depending on whether <code class="docutils literal notranslate"><span class="pre">epochs_pipeline</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> or not.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subjects</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of subject number</p></li>
+<li><p><strong>cache_config</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><a class="reference internal" href="moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><em>CacheConfig</em></a>) – Configuration for caching of datasets. See <code class="docutils literal notranslate"><span class="pre">CacheConfig</span></code>
+for details.</p></li>
+<li><p><strong>process_pipeline</strong> (<em>Pipeline</em><em> | </em><em>None</em>) – Optional processing pipeline to apply to the data.
+To generate an adequate pipeline, we recommend using
+<code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.utils.make_process_pipelines()</span></code>.
+This pipeline will receive <a class="reference external" href="https://mne.tools/stable/generated/mne.io.BaseRaw.html#mne.io.BaseRaw" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.io.BaseRaw</span></code></a> objects.
+The steps names of this pipeline should be elements of <code class="xref py py-class docutils literal notranslate"><span class="pre">StepType</span></code>.
+According to their name, the steps should either return a
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.BaseRaw.html#mne.io.BaseRaw" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.io.BaseRaw</span></code></a>, a <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a>, or a <code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.ndarray()</span></code>.
+This pipeline must be “fixed” because it will not be trained,
+i.e. no call to <code class="docutils literal notranslate"><span class="pre">fit</span></code> will be made.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>data</strong> – dict containing the raw data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-base-basedataset">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.base.BaseDataset</span></code><a class="headerlink" href="#examples-using-moabb-datasets-base-basedataset" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 4: Creating a dataset class"><img alt="Tutorial 4: Creating a dataset class" src="../_images/sphx_glr_4_adding_a_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/4_adding_a_dataset.html#sphx-glr-auto-tutorials-4-adding-a-dataset-py"><span class="std std-ref">Tutorial 4: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 4: Creating a dataset class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="moabb-datasets-base-cacheconfig">
+<h1>moabb.datasets.base.CacheConfig<a class="headerlink" href="#moabb-datasets-base-cacheconfig" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.base.CacheConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.base.</span></span><span class="sig-name descname"><span class="pre">CacheConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save_raw</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_epochs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite_raw</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite_epochs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/base.py#L21-L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.base.CacheConfig" title="Permalink to this definition">#</a></dt>
+<dd><p>Configuration for caching of datasets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>save_*</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – This flag specifies whether to save the output of the corresponding
+step to disk.</p></li>
+<li><p><strong>use</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – This flag specifies whether to use the disk cache in case it exists.
+If True, the Raw or Epochs objects returned will not be preloaded
+(this saves some time). Otherwise, they will be preloaded.
+If use is False, the save_* and overwrite_* keys will be ignored.</p></li>
+<li><p><strong>overwrite_*</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – This flag specifies whether to overwrite the disk cache in
+case it exist.</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(signifier)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Verbosity level. See mne.verbose.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.0.0.</span></p>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.datasets.base.CacheConfig.__hash__">
+<span class="sig-name descname"><span class="pre">__hash__</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#moabb.datasets.base.CacheConfig.__hash__" title="Permalink to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.base.CacheConfig.make">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">make</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dic</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.12)"><span class="pre">Dict</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><span class="pre">CacheConfig</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><span class="pre">CacheConfig</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/base.py#L69-L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.base.CacheConfig.make" title="Permalink to this definition">#</a></dt>
+<dd><p>Create a CacheConfig object from a dict or another CacheConfig object.</p>
+<p class="rubric">Examples</p>
+<p>Using default parameters:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">CacheConfig</span><span class="o">.</span><span class="n">make</span><span class="p">()</span>
+<span class="go">CacheConfig(save=True, use=True, overwrite=True, path=None)</span>
+</pre></div>
+</div>
+<p>From a dict:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dic</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;save&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">CacheConfig</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="n">dic</span><span class="p">)</span>
+<span class="go">CacheConfig(save=False, use=True, overwrite=True, path=None)</span>
+</pre></div>
+</div>
+</dd></dl>
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+                      </a>
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+  <section id="moabb-datasets-compound-dataset-bi2014a-il">
+<h1>moabb.datasets.compound_dataset.BI2014a_Il<a class="headerlink" href="#moabb-datasets-compound-dataset-bi2014a-il" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.compound_dataset.BI2014a_Il">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.compound_dataset.</span></span><span class="sig-name descname"><span class="pre">BI2014a_Il</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/compound_dataset/bi_illiteracy.py#L21-L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.compound_dataset.BI2014a_Il" title="Permalink to this definition">#</a></dt>
+<dd><p>A selection of subject from BI2014a with AUC &lt; 0.7 with pipeline:
+ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2014a_Il</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2014a_il</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2014a_il</span></code> will be removed in  version 1.1.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.compound_dataset.BI2014a_Il"><code class="docutils literal notranslate"><span class="pre">BI2014a_Il</span></code></a></li>
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+<h1>moabb.datasets.compound_dataset.BI2014b_Il<a class="headerlink" href="#moabb-datasets-compound-dataset-bi2014b-il" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.compound_dataset.BI2014b_Il">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.compound_dataset.</span></span><span class="sig-name descname"><span class="pre">BI2014b_Il</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/compound_dataset/bi_illiteracy.py#L51-L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.compound_dataset.BI2014b_Il" title="Permalink to this definition">#</a></dt>
+<dd><p>A selection of subject from BI2014b with AUC &lt; 0.7 with pipeline:
+ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2014b_Il</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2014b_il</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2014b_il</span></code> will be removed in  version 1.1.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.compound_dataset.BI2014b_Il"><code class="docutils literal notranslate"><span class="pre">BI2014b_Il</span></code></a></li>
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+  <section id="moabb-datasets-compound-dataset-bi2015a-il">
+<h1>moabb.datasets.compound_dataset.BI2015a_Il<a class="headerlink" href="#moabb-datasets-compound-dataset-bi2015a-il" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.compound_dataset.BI2015a_Il">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.compound_dataset.</span></span><span class="sig-name descname"><span class="pre">BI2015a_Il</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/compound_dataset/bi_illiteracy.py#L75-L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.compound_dataset.BI2015a_Il" title="Permalink to this definition">#</a></dt>
+<dd><p>A selection of subject from BI2015a with AUC &lt; 0.7 with pipeline:
+ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
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+  <section id="moabb-datasets-compound-dataset-bi2015b-il">
+<h1>moabb.datasets.compound_dataset.BI2015b_Il<a class="headerlink" href="#moabb-datasets-compound-dataset-bi2015b-il" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.compound_dataset.BI2015b_Il">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.compound_dataset.</span></span><span class="sig-name descname"><span class="pre">BI2015b_Il</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/compound_dataset/bi_illiteracy.py#L90-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.compound_dataset.BI2015b_Il" title="Permalink to this definition">#</a></dt>
+<dd><p>A selection of subject from BI2015b with AUC &lt; 0.7 with pipeline:
+ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI2015b_Il</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">bi2015b_il</span></code>. <code class="docutils literal notranslate"><span class="pre">bi2015b_il</span></code> will be removed in  version 1.1.</p>
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+  <section id="moabb-datasets-compound-dataset-bi-il">
+<h1>moabb.datasets.compound_dataset.BI_Il<a class="headerlink" href="#moabb-datasets-compound-dataset-bi-il" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.compound_dataset.BI_Il">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.compound_dataset.</span></span><span class="sig-name descname"><span class="pre">BI_Il</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/compound_dataset/bi_illiteracy.py#L145-L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.compound_dataset.BI_Il" title="Permalink to this definition">#</a></dt>
+<dd><p>Subjects from braininvaders datasets with AUC &lt; 0.7 with pipeline:
+ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">BI_Il</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">biIlliteracy</span></code>. <code class="docutils literal notranslate"><span class="pre">biIlliteracy</span></code> will be removed in  version 1.1.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.compound_dataset.BI_Il"><code class="docutils literal notranslate"><span class="pre">BI_Il</span></code></a></li>
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+  <section id="moabb-datasets-compound-dataset-cattan2019-vr-il">
+<h1>moabb.datasets.compound_dataset.Cattan2019_VR_Il<a class="headerlink" href="#moabb-datasets-compound-dataset-cattan2019-vr-il" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.compound_dataset.Cattan2019_VR_Il">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.compound_dataset.</span></span><span class="sig-name descname"><span class="pre">Cattan2019_VR_Il</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/compound_dataset/bi_illiteracy.py#L128-L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.compound_dataset.Cattan2019_VR_Il" title="Permalink to this definition">#</a></dt>
+<dd><p>A selection of subject from Cattan2019_VR with AUC &lt; 0.7 with pipeline:
+ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)</p>
+<p class="rubric">Notes</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">Cattan2019_VR_Il</span></code> was previously named <code class="docutils literal notranslate"><span class="pre">VirtualReality_il</span></code>. <code class="docutils literal notranslate"><span class="pre">VirtualReality_il</span></code> will be removed in  version 1.1.</p>
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+  <section id="moabb-datasets-download-data-dl">
+<h1>moabb.datasets.download.data_dl<a class="headerlink" href="#moabb-datasets-download-data-dl" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.data_dl">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">data_dl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sign</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../docs/&lt;decorator-gen-432&gt;#L105-L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.data_dl" title="Permalink to this definition">#</a></dt>
+<dd><p>Download file from url to specified path.</p>
+<p>This function should replace data_path as the MNE will not support the download
+of dataset anymore. This version is using Pooch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>url</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Path to remote location of data</p></li>
+<li><p><strong>sign</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Signifier of dataset</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(signifier)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level (see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a list
+of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-download-data-dl">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.download.data_dl</span></code><a class="headerlink" href="#examples-using-moabb-datasets-download-data-dl" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 4: Creating a dataset class"><img alt="Tutorial 4: Creating a dataset class" src="../_images/sphx_glr_4_adding_a_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/4_adding_a_dataset.html#sphx-glr-auto-tutorials-4-adding-a-dataset-py"><span class="std std-ref">Tutorial 4: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 4: Creating a dataset class</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+  <section id="moabb-datasets-download-data-path">
+<h1>moabb.datasets.download.data_path<a class="headerlink" href="#moabb-datasets-download-data-path" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.data_path">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sign</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../docs/&lt;decorator-gen-431&gt;#L59-L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of given dataset URL. <strong>Deprecated</strong></p>
+<p>This is a low-level function useful for getting a local copy of a
+remote dataset. It is deprecated in favor of data_dl.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>url</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Path to remote location of data</p></li>
+<li><p><strong>sign</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Signifier of dataset</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(signifier)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None, <strong>Deprecated</strong>) – Unused, kept for compatibility purpose.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level (see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a list
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+<h1>moabb.datasets.download.fs_get_file_hash<a class="headerlink" href="#moabb-datasets-download-fs-get-file-hash" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.fs_get_file_hash">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">fs_get_file_hash</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filelist</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/download.py#L235-L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.fs_get_file_hash" title="Permalink to this definition">#</a></dt>
+<dd><p>Returns a dict associating figshare file id to MD5 hash.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>filelist</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – HTTP request response from fs_get_file_list</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>response</strong> – keys are file_id and values are md5 hash</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
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+<h1>moabb.datasets.download.fs_get_file_id<a class="headerlink" href="#moabb-datasets-download-fs-get-file-id" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.fs_get_file_id">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">fs_get_file_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filelist</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/download.py#L251-L264"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.fs_get_file_id" title="Permalink to this definition">#</a></dt>
+<dd><p>Returns a dict associating filename to figshare file id.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>filelist</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – HTTP request response from fs_get_file_list</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>response</strong> – keys are filename and values are file_id</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
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+<h1>moabb.datasets.download.fs_get_file_list<a class="headerlink" href="#moabb-datasets-download-fs-get-file-list" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.fs_get_file_list">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">fs_get_file_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">article_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/download.py#L207-L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.fs_get_file_list" title="Permalink to this definition">#</a></dt>
+<dd><p>List all the files associated with a given article.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>article_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Figshare article ID</p></li>
+<li><p><strong>version</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>id</em><em>, </em><em>default is None</em>) – Figshare article version. If None, selects the most recent version.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>response</strong> – HTTP request response as a python dict</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
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+<h1>moabb.datasets.download.fs_get_file_name<a class="headerlink" href="#moabb-datasets-download-fs-get-file-name" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.fs_get_file_name">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">fs_get_file_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filelist</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/download.py#L267-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.fs_get_file_name" title="Permalink to this definition">#</a></dt>
+<dd><p>Returns a dict associating figshare file id to filename.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>filelist</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – HTTP request response from fs_get_file_list</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>response</strong> – keys are file_id and values are file name</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
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+  <section id="moabb-datasets-download-fs-issue-request">
+<h1>moabb.datasets.download.fs_issue_request<a class="headerlink" href="#moabb-datasets-download-fs-issue-request" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.download.fs_issue_request">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.download.</span></span><span class="sig-name descname"><span class="pre">fs_issue_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">headers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/download.py#L167-L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.download.fs_issue_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Wrapper for HTTP request.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – HTTP method. One of GET, PUT, POST or DELETE</p></li>
+<li><p><strong>url</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – URL for the request</p></li>
+<li><p><strong>headers</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – HTTP header information</p></li>
+<li><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Figshare article data</p></li>
+<li><p><strong>binary</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether data is binary or not</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>response_data</strong> – JSON response for the request returned as python dict</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.download.fs_issue_request"><code class="docutils literal notranslate"><span class="pre">fs_issue_request()</span></code></a></li>
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+  <section id="moabb-datasets-fake-fakedataset">
+<h1>moabb.datasets.fake.FakeDataset<a class="headerlink" href="#moabb-datasets-fake-fakedataset" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.fake.FakeDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.fake.</span></span><span class="sig-name descname"><span class="pre">FakeDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('fake1',</span> <span class="pre">'fake2',</span> <span class="pre">'fake3')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_sessions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_runs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_subjects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FakeDataset'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">paradigm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'imagery'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('C3',</span> <span class="pre">'Cz',</span> <span class="pre">'C4')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">annotations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/fake.py#L15-L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.fake.FakeDataset" title="Permalink to this definition">#</a></dt>
+<dd><p>Fake Dataset for test purpose.</p>
+<p>By default, the dataset has 2 sessions, 10 subjects, and 3 classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>event_list</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – List of event to generate, default: (“fake1”, “fake2”, “fake3”)</p></li>
+<li><p><strong>n_sessions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default 2</em>) – Number of session to generate</p></li>
+<li><p><strong>n_runs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default 2</em>) – Number of runs to generate</p></li>
+<li><p><strong>n_subjects</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default 10</em>) – Number of subject to generate</p></li>
+<li><p><strong>paradigm</strong> (<em>[</em><em>'p300'</em><em>,</em><em>'imagery'</em><em>, </em><em>'ssvep'</em><em>]</em>) – Defines what sort of dataset this is</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – List of channels to generate, default (“C3”, “Cz”, “C4”)</p></li>
+<li><p><strong>duration</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Duration of each run in seconds. If float, same duration for all
+runs. If list, duration for each run.</p></li>
+<li><p><strong>n_events</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of events per run. If int, same number of events
+for all runs. If list, number of events for each run.</p></li>
+<li><p><strong>stim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, pass events through stim channel.</p></li>
+<li><p><strong>annotations</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – <p>If True, pass events through Annotations.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.3.</span></p>
+</div>
+</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.fake.FakeDataset.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/fake.py#L154-L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.fake.FakeDataset.data_path" title="Permalink to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.6)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.datasets.fake.FakeDataset.data_path"><code class="docutils literal notranslate"><span class="pre">FakeDataset.data_path()</span></code></a></li>
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+  <section id="moabb-datasets-fake-fakevirtualrealitydataset">
+<h1>moabb.datasets.fake.FakeVirtualRealityDataset<a class="headerlink" href="#moabb-datasets-fake-fakevirtualrealitydataset" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.datasets.fake.FakeVirtualRealityDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.datasets.fake.</span></span><span class="sig-name descname"><span class="pre">FakeVirtualRealityDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/fake.py#L160-L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.fake.FakeVirtualRealityDataset" title="Permalink to this definition">#</a></dt>
+<dd><p>Fake Cattan2019_VR dataset for test purpose.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.datasets.fake.FakeVirtualRealityDataset.get_block_repetition">
+<span class="sig-name descname"><span class="pre">get_block_repetition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paradigm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subjects</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repetition_list</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/fake.py#L200-L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.fake.FakeVirtualRealityDataset.get_block_repetition" title="Permalink to this definition">#</a></dt>
+<dd><p>Select data for all provided subjects, blocks and repetitions. Each
+subject has 5 blocks of 12 repetitions.</p>
+<p>The returned data is a dictionary with the following structure:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;subject_id&#39;</span> <span class="p">:</span>
+            <span class="p">{</span><span class="s1">&#39;session_id&#39;</span><span class="p">:</span>
+                <span class="p">{</span><span class="s1">&#39;run_id&#39;</span><span class="p">:</span> <span class="n">raw</span><span class="p">}</span>
+            <span class="p">}</span>
+        <span class="p">}</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseDataset.get_data</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">Cattan2019_VR.get_block_repetition</span></code></p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subjects</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of subject number</p></li>
+<li><p><strong>block_list</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of block number (from 1 to 5)</p></li>
+<li><p><strong>repetition_list</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of repetition number inside a block (from 1 to 12)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>data</strong> – dict containing the raw data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Dict</p>
+</dd>
+</dl>
+</dd></dl>
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+<h1>moabb.datasets.utils.dataset_search<a class="headerlink" href="#moabb-datasets-utils-dataset-search" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.utils.dataset_search">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.utils.</span></span><span class="sig-name descname"><span class="pre">dataset_search</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paradigm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_session</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_all_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_subjects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/utils.py#L23-L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.utils.dataset_search" title="Permalink to this definition">#</a></dt>
+<dd><p>Returns a list of datasets that match a given criteria.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>paradigm</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – ‘imagery’, ‘p300’, ‘ssvep’, ‘cvep’, None</p></li>
+<li><p><strong>multi_session</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – if True only returns datasets with more than one session per subject.
+If False return all</p></li>
+<li><p><strong>events</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em>) – events to select</p></li>
+<li><p><strong>has_all_events</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – skip datasets that don’t have all events in events</p></li>
+<li><p><strong>interval</strong> – Length of motor imagery interval, in seconds. Only used in imagery
+paradigm</p></li>
+<li><p><strong>min_subjects</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em>) – minimum subjects in dataset</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – list or set of channels</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-utils-dataset-search">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.utils.dataset_search</span></code><a class="headerlink" href="#examples-using-moabb-datasets-utils-dataset-search" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
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+  <section id="moabb-datasets-utils-find-intersecting-channels">
+<h1>moabb.datasets.utils.find_intersecting_channels<a class="headerlink" href="#moabb-datasets-utils-find-intersecting-channels" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.datasets.utils.find_intersecting_channels">
+<span class="sig-prename descclassname"><span class="pre">moabb.datasets.utils.</span></span><span class="sig-name descname"><span class="pre">find_intersecting_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/utils.py#L117-L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.datasets.utils.find_intersecting_channels" title="Permalink to this definition">#</a></dt>
+<dd><p>Given a list of dataset instances return a list of channels shared by
+all datasets. Skip datasets which have 0 overlap with the others.</p>
+<p>returns: set of common channels, list of datasets with valid channels</p>
+</dd></dl>
+
+<section id="examples-using-moabb-datasets-utils-find-intersecting-channels">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.utils.find_intersecting_channels</span></code><a class="headerlink" href="#examples-using-moabb-datasets-utils-find-intersecting-channels" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-datasets-utils-find-intersecting-channels">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.datasets.utils.find_intersecting_channels</span></code></a></li>
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+  <section id="moabb-evaluations-crosssessionevaluation">
+<h1>moabb.evaluations.CrossSessionEvaluation<a class="headerlink" href="#moabb-evaluations-crosssessionevaluation" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.evaluations.CrossSessionEvaluation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.evaluations.</span></span><span class="sig-name descname"><span class="pre">CrossSessionEvaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paradigm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs_evaluation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additional_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mne_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L432-L596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.CrossSessionEvaluation" title="Permalink to this definition">#</a></dt>
+<dd><p>Cross-session performance evaluation.</p>
+<p>Evaluate performance of the pipeline across sessions but for a single
+subject. Verifies that there is at least two sessions before starting
+the evaluation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>paradigm</strong> (<em>Paradigm instance</em>) – The paradigm to use.</p></li>
+<li><p><strong>datasets</strong> (<em>List</em><em> of </em><em>Dataset instance</em>) – The list of dataset to run the evaluation. If none, the list of
+compatible dataset will be retrieved from the paradigm instance.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>RandomState instance</em><em>, </em><em>default=None</em>) – If not None, can guarantee same seed for shuffling examples.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for fitting of pipeline.</p></li>
+<li><p><strong>n_jobs_evaluation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for evaluation, processing in parallel the within session,
+cross-session or cross-subject.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – If true, overwrite the results.</p></li>
+<li><p><strong>error_score</strong> (<em>&quot;raise&quot;</em><em> or </em><em>numeric</em><em>, </em><em>default=&quot;raise&quot;</em>) – Value to assign to the score if an error occurs in estimator fitting. If set to
+‘raise’, the error is raised.</p></li>
+<li><p><strong>suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Suffix for the results file.</p></li>
+<li><p><strong>hdf5_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Specific path for storing the results and models.</p></li>
+<li><p><strong>additional_columns</strong> (<em>None</em>) – Adding information to results.</p></li>
+<li><p><strong>return_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE epoch to train pipelines.</p></li>
+<li><p><strong>return_raws</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE raw to train pipelines.</p></li>
+<li><p><strong>mne_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – if returning MNE epoch, use original dataset label if True</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.CrossSessionEvaluation.evaluate">
+<span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipelines</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_pipeline</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L473-L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.CrossSessionEvaluation.evaluate" title="Permalink to this definition">#</a></dt>
+<dd><p>Evaluate results on a single dataset.</p>
+<p>This method return a generator. each results item is a dict with
+the following conversion:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">Duration</span> <span class="n">of</span> <span class="n">the</span> <span class="n">training</span> <span class="p">,</span>
+       <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">dataset</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="n">subject</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;session&#39;</span><span class="p">:</span> <span class="n">session</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;score&#39;</span><span class="p">:</span> <span class="n">score</span><span class="p">,</span>
+       <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">training</span> <span class="n">examples</span><span class="p">,</span>
+       <span class="s1">&#39;n_channels&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">channel</span><span class="p">,</span>
+       <span class="s1">&#39;pipeline&#39;</span><span class="p">:</span> <span class="n">pipeline</span> <span class="n">name</span><span class="p">}</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.CrossSessionEvaluation.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L595-L596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.CrossSessionEvaluation.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with evaluation.</p>
+<p>This method is called to verify dataset given in the constructor
+are compatible with the evaluation context.</p>
+<p>This method should return false if the dataset does not match the
+evaluation. This is for example the case if the dataset does not
+contain enough session for a cross-session eval.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-evaluations-crosssessionevaluation">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.evaluations.CrossSessionEvaluation</span></code><a class="headerlink" href="#examples-using-moabb-evaluations-crosssessionevaluation" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+<h1>moabb.evaluations.CrossSubjectEvaluation<a class="headerlink" href="#moabb-evaluations-crosssubjectevaluation" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.evaluations.CrossSubjectEvaluation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.evaluations.</span></span><span class="sig-name descname"><span class="pre">CrossSubjectEvaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paradigm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs_evaluation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additional_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mne_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L599-L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.CrossSubjectEvaluation" title="Permalink to this definition">#</a></dt>
+<dd><p>Cross-subject evaluation performance.</p>
+<p>Evaluate performance of the pipeline trained on all subjects but one,
+concatenating sessions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>paradigm</strong> (<em>Paradigm instance</em>) – The paradigm to use.</p></li>
+<li><p><strong>datasets</strong> (<em>List</em><em> of </em><em>Dataset instance</em>) – The list of dataset to run the evaluation. If none, the list of
+compatible dataset will be retrieved from the paradigm instance.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>RandomState instance</em><em>, </em><em>default=None</em>) – If not None, can guarantee same seed for shuffling examples.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for fitting of pipeline.</p></li>
+<li><p><strong>n_jobs_evaluation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for evaluation, processing in parallel the within session,
+cross-session or cross-subject.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – If true, overwrite the results.</p></li>
+<li><p><strong>error_score</strong> (<em>&quot;raise&quot;</em><em> or </em><em>numeric</em><em>, </em><em>default=&quot;raise&quot;</em>) – Value to assign to the score if an error occurs in estimator fitting. If set to
+‘raise’, the error is raised.</p></li>
+<li><p><strong>suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Suffix for the results file.</p></li>
+<li><p><strong>hdf5_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Specific path for storing the results and models.</p></li>
+<li><p><strong>additional_columns</strong> (<em>None</em>) – Adding information to results.</p></li>
+<li><p><strong>return_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE epoch to train pipelines.</p></li>
+<li><p><strong>return_raws</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE raw to train pipelines.</p></li>
+<li><p><strong>mne_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – if returning MNE epoch, use original dataset label if True</p></li>
+<li><p><strong>n_splits</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=None</em>) – Number of splits for cross-validation. If None, the number of splits
+is equal to the number of subjects.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.CrossSubjectEvaluation.evaluate">
+<span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipelines</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_pipeline</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L642-L757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.CrossSubjectEvaluation.evaluate" title="Permalink to this definition">#</a></dt>
+<dd><p>Evaluate results on a single dataset.</p>
+<p>This method return a generator. each results item is a dict with
+the following conversion:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">Duration</span> <span class="n">of</span> <span class="n">the</span> <span class="n">training</span> <span class="p">,</span>
+       <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">dataset</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="n">subject</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;session&#39;</span><span class="p">:</span> <span class="n">session</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;score&#39;</span><span class="p">:</span> <span class="n">score</span><span class="p">,</span>
+       <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">training</span> <span class="n">examples</span><span class="p">,</span>
+       <span class="s1">&#39;n_channels&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">channel</span><span class="p">,</span>
+       <span class="s1">&#39;pipeline&#39;</span><span class="p">:</span> <span class="n">pipeline</span> <span class="n">name</span><span class="p">}</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.CrossSubjectEvaluation.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L759-L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.CrossSubjectEvaluation.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with evaluation.</p>
+<p>This method is called to verify dataset given in the constructor
+are compatible with the evaluation context.</p>
+<p>This method should return false if the dataset does not match the
+evaluation. This is for example the case if the dataset does not
+contain enough session for a cross-session eval.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-evaluations-crosssubjectevaluation">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.evaluations.CrossSubjectEvaluation</span></code><a class="headerlink" href="#examples-using-moabb-evaluations-crosssubjectevaluation" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
+                </article>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.evaluations.CrossSubjectEvaluation.evaluate"><code class="docutils literal notranslate"><span class="pre">CrossSubjectEvaluation.evaluate()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.evaluations.CrossSubjectEvaluation.is_valid"><code class="docutils literal notranslate"><span class="pre">CrossSubjectEvaluation.is_valid()</span></code></a></li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-evaluations-crosssubjectevaluation">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.evaluations.CrossSubjectEvaluation</span></code></a></li>
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+  <section id="moabb-evaluations-withinsessionevaluation">
+<h1>moabb.evaluations.WithinSessionEvaluation<a class="headerlink" href="#moabb-evaluations-withinsessionevaluation" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.evaluations.WithinSessionEvaluation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.evaluations.</span></span><span class="sig-name descname"><span class="pre">WithinSessionEvaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_perms</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L38-L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.WithinSessionEvaluation" title="Permalink to this definition">#</a></dt>
+<dd><p>Performance evaluation within session (k-fold cross-validation)</p>
+<p>Within-session evaluation uses k-fold cross_validation to determine train
+and test sets on separate session for each subject, it is possible to
+estimate the performance on a subset of training examples to obtain
+learning curves.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_perms</strong> – Number of permutations to perform. If an array
+is passed it has to be equal in size to the data_size array.
+Values in this array must be monotonically decreasing (performing
+more permutations for more data is not useful to reduce standard
+error of the mean).
+Default: None</p></li>
+<li><p><strong>data_size</strong> – If None is passed, it performs conventional WithinSession evaluation.
+Contains the policy to pick the datasizes to
+evaluate, as well as the actual values. The dict has the
+key ‘policy’ with either ‘ratio’ or ‘per_class’, and the key
+‘value’ with the actual values as an numpy array. This array should be
+sorted, such that values in data_size are strictly monotonically increasing.
+Default: None</p></li>
+<li><p><strong>paradigm</strong> (<em>Paradigm instance</em>) – The paradigm to use.</p></li>
+<li><p><strong>datasets</strong> (<em>List</em><em> of </em><em>Dataset instance</em>) – The list of dataset to run the evaluation. If none, the list of
+compatible dataset will be retrieved from the paradigm instance.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>RandomState instance</em><em>, </em><em>default=None</em>) – If not None, can guarantee same seed for shuffling examples.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for fitting of pipeline.</p></li>
+<li><p><strong>n_jobs_evaluation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for evaluation, processing in parallel the within session,
+cross-session or cross-subject.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – If true, overwrite the results.</p></li>
+<li><p><strong>error_score</strong> (<em>&quot;raise&quot;</em><em> or </em><em>numeric</em><em>, </em><em>default=&quot;raise&quot;</em>) – Value to assign to the score if an error occurs in estimator fitting. If set to
+‘raise’, the error is raised.</p></li>
+<li><p><strong>suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Suffix for the results file.</p></li>
+<li><p><strong>hdf5_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Specific path for storing the results and models.</p></li>
+<li><p><strong>additional_columns</strong> (<em>None</em>) – Adding information to results.</p></li>
+<li><p><strong>return_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE epoch to train pipelines.</p></li>
+<li><p><strong>return_raws</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE raw to train pipelines.</p></li>
+<li><p><strong>mne_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – if returning MNE epoch, use original dataset label if True</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.WithinSessionEvaluation.evaluate">
+<span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipelines</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_pipeline</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L416-L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.WithinSessionEvaluation.evaluate" title="Permalink to this definition">#</a></dt>
+<dd><p>Evaluate results on a single dataset.</p>
+<p>This method return a generator. each results item is a dict with
+the following conversion:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">Duration</span> <span class="n">of</span> <span class="n">the</span> <span class="n">training</span> <span class="p">,</span>
+       <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">dataset</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="n">subject</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;session&#39;</span><span class="p">:</span> <span class="n">session</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;score&#39;</span><span class="p">:</span> <span class="n">score</span><span class="p">,</span>
+       <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">training</span> <span class="n">examples</span><span class="p">,</span>
+       <span class="s1">&#39;n_channels&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">channel</span><span class="p">,</span>
+       <span class="s1">&#39;pipeline&#39;</span><span class="p">:</span> <span class="n">pipeline</span> <span class="n">name</span><span class="p">}</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.WithinSessionEvaluation.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/evaluations.py#L428-L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.WithinSessionEvaluation.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with evaluation.</p>
+<p>This method is called to verify dataset given in the constructor
+are compatible with the evaluation context.</p>
+<p>This method should return false if the dataset does not match the
+evaluation. This is for example the case if the dataset does not
+contain enough session for a cross-session eval.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-evaluations-withinsessionevaluation">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.evaluations.WithinSessionEvaluation</span></code><a class="headerlink" href="#examples-using-moabb-evaluations-withinsessionevaluation" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 4: Creating a dataset class"><img alt="Tutorial 4: Creating a dataset class" src="../_images/sphx_glr_4_adding_a_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/4_adding_a_dataset.html#sphx-glr-auto-tutorials-4-adding-a-dataset-py"><span class="std std-ref">Tutorial 4: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 4: Creating a dataset class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 5: Creating a dataset class"><img alt="Tutorial 5: Creating a dataset class" src="../_images/sphx_glr_noplot_tutorial_5_build_a_custom_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html#sphx-glr-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"><span class="std std-ref">Tutorial 5: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 5: Creating a dataset class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
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+  <section id="moabb-evaluations-base-baseevaluation">
+<h1>moabb.evaluations.base.BaseEvaluation<a class="headerlink" href="#moabb-evaluations-base-baseevaluation" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.evaluations.base.BaseEvaluation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.evaluations.base.</span></span><span class="sig-name descname"><span class="pre">BaseEvaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paradigm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs_evaluation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additional_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mne_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/base.py#L16-L274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.base.BaseEvaluation" title="Permalink to this definition">#</a></dt>
+<dd><p>Base class that defines necessary operations for an evaluation.
+Evaluations determine what the train and test sets are and can implement
+additional data preprocessing steps for more complicated algorithms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>paradigm</strong> (<em>Paradigm instance</em>) – The paradigm to use.</p></li>
+<li><p><strong>datasets</strong> (<em>List</em><em> of </em><em>Dataset instance</em>) – The list of dataset to run the evaluation. If none, the list of
+compatible dataset will be retrieved from the paradigm instance.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>RandomState instance</em><em>, </em><em>default=None</em>) – If not None, can guarantee same seed for shuffling examples.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for fitting of pipeline.</p></li>
+<li><p><strong>n_jobs_evaluation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Number of jobs for evaluation, processing in parallel the within session,
+cross-session or cross-subject.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – If true, overwrite the results.</p></li>
+<li><p><strong>error_score</strong> (<em>&quot;raise&quot;</em><em> or </em><em>numeric</em><em>, </em><em>default=&quot;raise&quot;</em>) – Value to assign to the score if an error occurs in estimator fitting. If set to
+‘raise’, the error is raised.</p></li>
+<li><p><strong>suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Suffix for the results file.</p></li>
+<li><p><strong>hdf5_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Specific path for storing the results.</p></li>
+<li><p><strong>additional_columns</strong> (<em>None</em>) – Adding information to results.</p></li>
+<li><p><strong>return_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE epoch to train pipelines.</p></li>
+<li><p><strong>return_raws</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – use MNE raw to train pipelines.</p></li>
+<li><p><strong>mne_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – if returning MNE epoch, use original dataset label if True</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.base.BaseEvaluation.evaluate">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipelines</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_pipeline</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/base.py#L219-L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.base.BaseEvaluation.evaluate" title="Permalink to this definition">#</a></dt>
+<dd><p>Evaluate results on a single dataset.</p>
+<p>This method return a generator. each results item is a dict with
+the following conversion:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">Duration</span> <span class="n">of</span> <span class="n">the</span> <span class="n">training</span> <span class="p">,</span>
+       <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">dataset</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="n">subject</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;session&#39;</span><span class="p">:</span> <span class="n">session</span> <span class="nb">id</span><span class="p">,</span>
+       <span class="s1">&#39;score&#39;</span><span class="p">:</span> <span class="n">score</span><span class="p">,</span>
+       <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">training</span> <span class="n">examples</span><span class="p">,</span>
+       <span class="s1">&#39;n_channels&#39;</span><span class="p">:</span> <span class="n">number</span> <span class="n">of</span> <span class="n">channel</span><span class="p">,</span>
+       <span class="s1">&#39;pipeline&#39;</span><span class="p">:</span> <span class="n">pipeline</span> <span class="n">name</span><span class="p">}</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.base.BaseEvaluation.is_valid">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/base.py#L239-L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.base.BaseEvaluation.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with evaluation.</p>
+<p>This method is called to verify dataset given in the constructor
+are compatible with the evaluation context.</p>
+<p>This method should return false if the dataset does not match the
+evaluation. This is for example the case if the dataset does not
+contain enough session for a cross-session eval.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.evaluations.base.BaseEvaluation.process">
+<span class="sig-name descname"><span class="pre">process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pipelines</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/evaluations/base.py#L141-L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.evaluations.base.BaseEvaluation.process" title="Permalink to this definition">#</a></dt>
+<dd><p>Runs all pipelines on all datasets.</p>
+<p>This function will apply all provided pipelines and return a dataframe
+containing the results of the evaluation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pipelines</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> of </em><em>pipeline instance.</em>) – A dict containing the sklearn pipeline to evaluate.</p></li>
+<li><p><strong>param_grid</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The key of the dictionary must be the same as the associated pipeline.</p></li>
+<li><p><strong>postprocess_pipeline</strong> (<em>Pipeline</em><em> | </em><em>None</em>) – Optional pipeline to apply to the data after the preprocessing.
+This pipeline will either receive <a class="reference external" href="https://mne.tools/stable/generated/mne.io.BaseRaw.html#mne.io.BaseRaw" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.io.BaseRaw</span></code></a>, <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a>
+or <code class="xref py py-func docutils literal notranslate"><span class="pre">np.ndarray()</span></code> as input, depending on the values of <code class="docutils literal notranslate"><span class="pre">return_epochs</span></code>
+and <code class="docutils literal notranslate"><span class="pre">return_raws</span></code>.
+This pipeline must return an <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>.
+This pipeline must be “fixed” because it will not be trained,
+i.e. no call to <code class="docutils literal notranslate"><span class="pre">fit</span></code> will be made.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>results</strong> – A dataframe containing the results.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-evaluations-base-baseevaluation">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.evaluations.base.BaseEvaluation</span></code><a class="headerlink" href="#examples-using-moabb-evaluations-base-baseevaluation" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
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+  <section id="moabb-make-process-pipelines">
+<h1>moabb.make_process_pipelines<a class="headerlink" href="#moabb-make-process-pipelines" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.make_process_pipelines">
+<span class="sig-prename descclassname"><span class="pre">moabb.</span></span><span class="sig-name descname"><span class="pre">make_process_pipelines</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">processing</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing" title="moabb.paradigms.base.BaseProcessing"><span class="pre">BaseProcessing</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset"><span class="pre">BaseDataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/utils.py#L164-L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.make_process_pipelines" title="Permalink to this definition">#</a></dt>
+<dd><p>Shortcut for the method <a class="reference internal" href="moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.make_process_pipelines" title="moabb.paradigms.base.BaseProcessing.make_process_pipelines"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseProcessing.make_process_pipelines()</span></code></a></p>
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diff --git a/docs/generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html b/docs/generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html
new file mode 100644
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+  <section id="moabb-paradigms-basefixedintervalwindowsprocessing">
+<h1>moabb.paradigms.BaseFixedIntervalWindowsProcessing<a class="headerlink" href="#moabb-paradigms-basefixedintervalwindowsprocessing" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.BaseFixedIntervalWindowsProcessing">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">BaseFixedIntervalWindowsProcessing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/fixed_interval_windows.py#L6-L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.BaseFixedIntervalWindowsProcessing" title="Permalink to this definition">#</a></dt>
+<dd><p>Base class for fixed interval windows processing.</p>
+<p>Paradigm for creating epochs at fixed interval,
+ignoring the stim channel and events of the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>default</em><em> [</em><em>[</em><em>7</em><em>, </em><em>45</em><em>]</em><em>]</em><em>)</em>) – bank of bandpass filter to apply.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+<li><p><strong>length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 5.0</em><em>)</em>) – Length of the epochs in seconds.</p></li>
+<li><p><strong>stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 10.0</em><em>)</em>) – Stride between epochs in seconds.</p></li>
+<li><p><strong>start_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start from the beginning of the raw recordings in seconds.</p></li>
+<li><p><strong>stop_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Stop offset from beginning of raw recordings in seconds.
+If None, set to be the end of the recording.</p></li>
+<li><p><strong>marker</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default -1</em><em>)</em>) – Marker to use for the events created.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.0.0.</span></p>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.BaseFixedIntervalWindowsProcessing.datasets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.BaseFixedIntervalWindowsProcessing.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that define the list of compatible datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.BaseFixedIntervalWindowsProcessing.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/fixed_interval_windows.py#L113-L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.BaseFixedIntervalWindowsProcessing.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with the paradigm.</p>
+<p>This method is called to verify dataset is compatible with the
+paradigm.</p>
+<p>This method should raise an error if the dataset is not compatible
+with the paradigm. This is for example the case if the
+dataset is an ERP dataset for motor imagery paradigm, or if the
+dataset does not contain any of the required events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-basefixedintervalwindowsprocessing">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.BaseFixedIntervalWindowsProcessing</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-basefixedintervalwindowsprocessing" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.BaseFixedIntervalWindowsProcessing"><code class="docutils literal notranslate"><span class="pre">BaseFixedIntervalWindowsProcessing</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.BaseFixedIntervalWindowsProcessing.datasets"><code class="docutils literal notranslate"><span class="pre">BaseFixedIntervalWindowsProcessing.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.BaseFixedIntervalWindowsProcessing.is_valid"><code class="docutils literal notranslate"><span class="pre">BaseFixedIntervalWindowsProcessing.is_valid()</span></code></a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-paradigms-basefixedintervalwindowsprocessing">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.BaseFixedIntervalWindowsProcessing</span></code></a></li>
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+  <section id="moabb-paradigms-cvep">
+<h1>moabb.paradigms.CVEP<a class="headerlink" href="#moabb-paradigms-cvep" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.CVEP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">CVEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">45.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/cvep.py#L162-L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.CVEP" title="Permalink to this definition">#</a></dt>
+<dd><p>Single bandpass c-VEP paradigm for epoch-level decoding.</p>
+<p>c-VEP paradigm with only one bandpass filter (default 1 to 45 Hz)
+Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more.
+This paradigm is meant to be used for epoch-level decoding of c-VEP
+datasets; this means that the goal of the classifiers with this paradigm
+is to predict, for every stimulation one by one, the amplitude of
+the target code. The code value is between 0  (i.e., stimulation off)
+and 1 (i.e., maximal stimulation).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 1.0</em><em>)</em>) – cutoff frequency (Hz) for the highpass filter</p></li>
+<li><p><strong>fmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 45.0</em><em>)</em>) – cutoff frequency (Hz) for the lowpass filter</p></li>
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Event to use for epoching. Note, we stick to a convention where the
+intensity level is encoded as float. For example, a binary sequence
+would have events 1.0 (i.e., on) and 0.0 (i.e., off). If None, default
+to all events defined in the dataset.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Number of classes each dataset must have. All dataset classes if None</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em><em> (</em><em>default None</em><em>)</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
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+  <section id="moabb-paradigms-filterbankcvep">
+<h1>moabb.paradigms.FilterBankCVEP<a class="headerlink" href="#moabb-paradigms-filterbankcvep" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankCVEP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">FilterBankCVEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">((1.0,</span> <span class="pre">45.0),</span> <span class="pre">(12.0,</span> <span class="pre">45.0),</span> <span class="pre">(30.0,</span> <span class="pre">45.0))</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/cvep.py#L224-L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FilterBankCVEP" title="Permalink to this definition">#</a></dt>
+<dd><p>Filterbank c-VEP paradigm for epoch-level decoding.</p>
+<p>c-VEP paradigm with multiple bandpass filters.
+Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more.
+This paradigm is meant to be used for epoch-level decoding of c-VEP
+datasets; this means that the goal of the classifiers with this paradigm
+is to predict, for every stimulation one by one, the amplitude of
+the target code. The code value is between 0  (i.e., stimulation off)
+and 1 (i.e., maximal stimulation).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> | </em><em>None</em><em> (</em><em>default</em><em> (</em><em>(</em><em>1.</em><em>, </em><em>45.</em><em>)</em><em>, </em><em>(</em><em>12.</em><em>, </em><em>45.</em><em>)</em><em>, </em><em>(</em><em>30.</em><em>, </em><em>45.</em><em>)</em><em>)</em><em>)</em>) – Bank of bandpass filter to apply.</p></li>
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Event to use for epoching. Note, we stick to a convention where the
+intensity level is encoded as float. For example, a binary sequence
+would have events 1.0 (i.e., on) and 0.0 (i.e., off). If None, default
+to all events defined in the dataset.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Number of classes each dataset must have. All dataset classes if None</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em><em> (</em><em>default None</em><em>)</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
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+  <section id="moabb-paradigms-filterbankfixedintervalwindowsprocessing">
+<h1>moabb.paradigms.FilterBankFixedIntervalWindowsProcessing<a class="headerlink" href="#moabb-paradigms-filterbankfixedintervalwindowsprocessing" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankFixedIntervalWindowsProcessing">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">FilterBankFixedIntervalWindowsProcessing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">((8,</span> <span class="pre">12),</span> <span class="pre">(12,</span> <span class="pre">16),</span> <span class="pre">(16,</span> <span class="pre">20),</span> <span class="pre">(20,</span> <span class="pre">24),</span> <span class="pre">(24,</span> <span class="pre">28),</span> <span class="pre">(28,</span> <span class="pre">32))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/fixed_interval_windows.py#L203-L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FilterBankFixedIntervalWindowsProcessing" title="Permalink to this definition">#</a></dt>
+<dd><p>Filter bank fixed interval windows processing.</p>
+<p>Paradigm for creating epochs at fixed interval
+with multiple narrow bandpass filters,
+ignoring the stim channel and events of the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>default</em><em> (</em><em>(</em><em>8</em><em>, </em><em>12</em><em>)</em><em>, </em><em>(</em><em>12</em><em>, </em><em>16</em><em>)</em><em>, </em><em>(</em><em>16</em><em>, </em><em>20</em><em>)</em><em>, </em><em>(</em><em>20</em><em>, </em><em>24</em><em>)</em><em>, </em><em>(</em><em>24</em><em>, </em><em>28</em><em>)</em><em>, </em><em>(</em><em>28</em><em>, </em><em>32</em><em>)</em><em>)</em><em>)</em>) – bank of bandpass filter to apply.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+<li><p><strong>length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 5.0</em><em>)</em>) – Length of the epochs in seconds.</p></li>
+<li><p><strong>stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 10.0</em><em>)</em>) – Stride between epochs in seconds.</p></li>
+<li><p><strong>start_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start from the beginning of the raw recordings in seconds.</p></li>
+<li><p><strong>stop_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Stop offset from beginning of raw recordings in seconds.
+If None, set to be the end of the recording.</p></li>
+<li><p><strong>marker</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default -1</em><em>)</em>) – Marker to use for the events created.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-filterbankfixedintervalwindowsprocessing">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-filterbankfixedintervalwindowsprocessing" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+  <section id="moabb-paradigms-filterbankleftrightimagery">
+<h1>moabb.paradigms.FilterBankLeftRightImagery<a class="headerlink" href="#moabb-paradigms-filterbankleftrightimagery" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankLeftRightImagery">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">FilterBankLeftRightImagery</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L189-L205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FilterBankLeftRightImagery" title="Permalink to this definition">#</a></dt>
+<dd><p>Filter Bank Motor Imagery for left hand/right hand classification.</p>
+<p>Metric is ‘roc_auc’</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankLeftRightImagery.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.FilterBankLeftRightImagery.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-filterbankleftrightimagery">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankLeftRightImagery</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-filterbankleftrightimagery" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
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+  <section id="moabb-paradigms-filterbankmotorimagery">
+<h1>moabb.paradigms.FilterBankMotorImagery<a class="headerlink" href="#moabb-paradigms-filterbankmotorimagery" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankMotorImagery">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">FilterBankMotorImagery</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L208-L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FilterBankMotorImagery" title="Permalink to this definition">#</a></dt>
+<dd><p>Filter bank n-class motor imagery.</p>
+<p>Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – event labels used to filter datasets (e.g. if only motor imagery is
+desired).</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em>) – number of classes each dataset must have. If events is given,
+requires all imagery sorts to be within the events list.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankMotorImagery.datasets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.FilterBankMotorImagery.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that define the list of compatible datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankMotorImagery.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L235-L246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FilterBankMotorImagery.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with the paradigm.</p>
+<p>This method is called to verify dataset is compatible with the
+paradigm.</p>
+<p>This method should raise an error if the dataset is not compatible
+with the paradigm. This is for example the case if the
+dataset is an ERP dataset for motor imagery paradigm, or if the
+dataset does not contain any of the required events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankMotorImagery.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.FilterBankMotorImagery.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-filterbankmotorimagery">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankMotorImagery</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-filterbankmotorimagery" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.FilterBankMotorImagery"><code class="docutils literal notranslate"><span class="pre">FilterBankMotorImagery</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.FilterBankMotorImagery.datasets"><code class="docutils literal notranslate"><span class="pre">FilterBankMotorImagery.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.FilterBankMotorImagery.is_valid"><code class="docutils literal notranslate"><span class="pre">FilterBankMotorImagery.is_valid()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.FilterBankMotorImagery.scoring"><code class="docutils literal notranslate"><span class="pre">FilterBankMotorImagery.scoring</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-paradigms-filterbankmotorimagery">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankMotorImagery</span></code></a></li>
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+  <section id="moabb-paradigms-filterbankssvep">
+<h1>moabb.paradigms.FilterBankSSVEP<a class="headerlink" href="#moabb-paradigms-filterbankssvep" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.FilterBankSSVEP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">FilterBankSSVEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/ssvep.py#L230-L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FilterBankSSVEP" title="Permalink to this definition">#</a></dt>
+<dd><p>Filtered bank n-class SSVEP paradigm.</p>
+<p>SSVEP paradigm with multiple narrow bandpass filters, centered around the
+frequencies of considered events.
+Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more.
+:param filters: If None, bandpass set around freqs of events with [f_n-0.5, f_n+0.5]
+:type filters: list of list | None (default None)
+:param events: List of stimulation frequencies. If None, use all stimulus</p>
+<blockquote>
+<div><p>found in the dataset.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>None</em><em> (</em><em>default 2</em><em>)</em>) – Number of classes each dataset must have. All dataset classes if None</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-filterbankssvep">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankSSVEP</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-filterbankssvep" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+  <section id="moabb-paradigms-fixedintervalwindowsprocessing">
+<h1>moabb.paradigms.FixedIntervalWindowsProcessing<a class="headerlink" href="#moabb-paradigms-fixedintervalwindowsprocessing" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.FixedIntervalWindowsProcessing">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">FixedIntervalWindowsProcessing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">45</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/fixed_interval_windows.py#L130-L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.FixedIntervalWindowsProcessing" title="Permalink to this definition">#</a></dt>
+<dd><p>Fixed interval windows processing.</p>
+<p>Paradigm for creating epochs at fixed interval,
+ignoring the stim channel and events of the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>fmin: float (default 7)</strong></dt><dd><p>cutoff frequency (Hz) for the high pass filter</p>
+</dd>
+<dt><strong>fmax: float (default 45)</strong></dt><dd><p>cutoff frequency (Hz) for the low pass filter</p>
+</dd>
+<dt><strong>baseline: None | tuple of length 2</strong></dt><dd><p>The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p>
+</dd>
+<dt><strong>channels: list of str | None (default None)</strong></dt><dd><p>list of channel to select. If None, use all EEG channels available in
+the dataset.</p>
+</dd>
+<dt><strong>resample: float | None (default None)</strong></dt><dd><p>If not None, resample the eeg data with the sampling rate provided.</p>
+</dd>
+<dt><strong>length: float (default 5.0)</strong></dt><dd><p>Length of the epochs in seconds.</p>
+</dd>
+<dt><strong>stride: float (default 10.0)</strong></dt><dd><p>Stride between epochs in seconds.</p>
+</dd>
+<dt><strong>start_offset: float (default 0.0)</strong></dt><dd><p>Start from the beginning of the raw recordings in seconds.</p>
+</dd>
+<dt><strong>stop_offset: float | None (default None)</strong></dt><dd><p>Stop offset from beginning of raw recordings in seconds.
+If None, set to be the end of the recording.</p>
+</dd>
+<dt><strong>marker: int (default -1)</strong></dt><dd><p>Marker to use for the events created.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-fixedintervalwindowsprocessing">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.FixedIntervalWindowsProcessing</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-fixedintervalwindowsprocessing" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+  <section id="moabb-paradigms-leftrightimagery">
+<h1>moabb.paradigms.LeftRightImagery<a class="headerlink" href="#moabb-paradigms-leftrightimagery" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.LeftRightImagery">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">LeftRightImagery</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L170-L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.LeftRightImagery" title="Permalink to this definition">#</a></dt>
+<dd><p>Motor Imagery for left hand/right hand classification.</p>
+<p>Metric is ‘roc_auc’</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.LeftRightImagery.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.LeftRightImagery.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-leftrightimagery">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.LeftRightImagery</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-leftrightimagery" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to track the CO2 footprint using `CodeCarbon library &lt;https..."><img alt="Benchmarking with MOABB showing the CO2 footprint" src="../_images/sphx_glr_example_codecarbon_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB showing the CO2 footprint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 4: Creating a dataset class"><img alt="Tutorial 4: Creating a dataset class" src="../_images/sphx_glr_4_adding_a_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/4_adding_a_dataset.html#sphx-glr-auto-tutorials-4-adding-a-dataset-py"><span class="std std-ref">Tutorial 4: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 4: Creating a dataset class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.LeftRightImagery"><code class="docutils literal notranslate"><span class="pre">LeftRightImagery</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.LeftRightImagery.scoring"><code class="docutils literal notranslate"><span class="pre">LeftRightImagery.scoring</span></code></a></li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-paradigms-leftrightimagery">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.LeftRightImagery</span></code></a></li>
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+  <section id="moabb-paradigms-motorimagery">
+<h1>moabb.paradigms.MotorImagery<a class="headerlink" href="#moabb-paradigms-motorimagery" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.MotorImagery">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">MotorImagery</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L291-L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.MotorImagery" title="Permalink to this definition">#</a></dt>
+<dd><p>N-class motor imagery.</p>
+<p>Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – event labels used to filter datasets (e.g. if only motor imagery is
+desired).</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em>) – number of classes each dataset must have. If events is given,
+requires all imagery sorts to be within the events list.</p></li>
+<li><p><strong>fmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 8</em><em>)</em>) – cutoff frequency (Hz) for the high pass filter</p></li>
+<li><p><strong>fmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 32</em><em>)</em>) – cutoff frequency (Hz) for the low pass filter</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.MotorImagery.datasets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.MotorImagery.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that define the list of compatible datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.MotorImagery.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L349-L362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.MotorImagery.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with the paradigm.</p>
+<p>This method is called to verify dataset is compatible with the
+paradigm.</p>
+<p>This method should raise an error if the dataset is not compatible
+with the paradigm. This is for example the case if the
+dataset is an ERP dataset for motor imagery paradigm, or if the
+dataset does not contain any of the required events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.MotorImagery.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.MotorImagery.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-motorimagery">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.MotorImagery</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-motorimagery" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
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+  <section id="moabb-paradigms-p300">
+<h1>moabb.paradigms.P300<a class="headerlink" href="#moabb-paradigms-p300" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.P300">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">P300</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/p300.py#L166-L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.P300" title="Permalink to this definition">#</a></dt>
+<dd><p>P300 for Target/NonTarget classification.</p>
+<p>Metric is ‘roc_auc’</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.P300.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.P300.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-p300">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.P300</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-p300" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="======================================= Changing epoch size in P300 VR dataset ================..."><img alt="Example of P300 classification with different epoch size." src="../_images/sphx_glr_noplot_vr_pc_p300_different_epoch_size_thumb.png" />
+<p><a class="reference external" href="https://neurotechx.github.io/moabb/auto_examples/noplot_vr_pc_p300_different_epoch_size.html#sphx-glr-auto-examples-noplot-vr-pc-p300-different-epoch-size-py" title="(in moabb v1.0.0)"><span class="xref std std-ref">sphx_glr_auto_examples_noplot_vr_pc_p300_different_epoch_size.py</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Example of P300 classification with different epoch size.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tutorial 5: Creating a dataset class"><img alt="Tutorial 5: Creating a dataset class" src="../_images/sphx_glr_noplot_tutorial_5_build_a_custom_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html#sphx-glr-auto-tutorials-noplot-tutorial-5-build-a-custom-dataset-py"><span class="std std-ref">Tutorial 5: Creating a dataset class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 5: Creating a dataset class</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="moabb-paradigms-ssvep">
+<h1>moabb.paradigms.SSVEP<a class="headerlink" href="#moabb-paradigms-ssvep" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.SSVEP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">SSVEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">45</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/ssvep.py#L175-L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.SSVEP" title="Permalink to this definition">#</a></dt>
+<dd><p>Single bandpass filter SSVEP.</p>
+<p>SSVEP paradigm with only one bandpass filter (default 7 to 45 Hz)
+Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 7</em><em>)</em>) – cutoff frequency (Hz) for the high pass filter</p></li>
+<li><p><strong>fmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 45</em><em>)</em>) – cutoff frequency (Hz) for the low pass filter</p></li>
+<li><p><strong>events</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of stimulation frequencies. If None, use all stimulus
+found in the dataset.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Number of classes each dataset must have. All dataset classes if None</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-ssvep">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.SSVEP</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-ssvep" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
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+  <section id="moabb-paradigms-singlepass">
+<h1>moabb.paradigms.SinglePass<a class="headerlink" href="#moabb-paradigms-singlepass" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.SinglePass">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.</span></span><span class="sig-name descname"><span class="pre">SinglePass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">24</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/p300.py#L108-L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.SinglePass" title="Permalink to this definition">#</a></dt>
+<dd><p>Single Bandpass filter P300.</p>
+<p>P300 paradigm with only one bandpass filter (default 1 to 24 Hz)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 1</em><em>)</em>) – cutoff frequency (Hz) for the high pass filter</p></li>
+<li><p><strong>fmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 24</em><em>)</em>) – cutoff frequency (Hz) for the low pass filter</p></li>
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – event to use for epoching. If None, default to all events defined in
+the dataset.</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-singlepass">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.SinglePass</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-singlepass" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="moabb-paradigms-base-baseparadigm">
+<h1>moabb.paradigms.base.BaseParadigm<a class="headerlink" href="#moabb-paradigms-base-baseparadigm" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseParadigm">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.base.</span></span><span class="sig-name descname"><span class="pre">BaseParadigm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">events</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L501-L544"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseParadigm" title="Permalink to this definition">#</a></dt>
+<dd><p>Base class for paradigms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – event to use for epoching. If None, default to all events defined in
+the dataset.</p>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseParadigm.scoring">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.base.BaseParadigm.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
+                </article>
+              
+              
+              
+                <footer class="bd-footer-article">
+                  
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+  <section id="moabb-paradigms-base-baseprocessing">
+<h1>moabb.paradigms.base.BaseProcessing<a class="headerlink" href="#moabb-paradigms-base-baseprocessing" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.base.</span></span><span class="sig-name descname"><span class="pre">BaseProcessing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L30-L498"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing" title="Permalink to this definition">#</a></dt>
+<dd><p>Base Processing.</p>
+<p>Please use one of the child classes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>defaults</em><em> [</em><em>[</em><em>7</em><em>, </em><em>35</em><em>]</em><em>]</em><em>)</em>) – bank of bandpass filter to apply.</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.datasets">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that define the list of compatible datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.get_data">
+<span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subjects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L213-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.get_data" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the data for a list of subject.</p>
+<p>return the data, labels and a dataframe with metadata. the dataframe
+will contain at least the following columns</p>
+<ul class="simple">
+<li><p>subject : the subject indice</p></li>
+<li><p>session : the session indice</p></li>
+<li><p>run : the run indice</p></li>
+</ul>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – A dataset instance.</p></li>
+<li><p><strong>subjects</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – List of subject number</p></li>
+<li><p><strong>return_epochs</strong> (<em>boolean</em>) – This flag specifies whether to return only the data array or the
+complete processed mne.Epochs</p></li>
+<li><p><strong>return_raws</strong> (<em>boolean</em>) – To return raw files and events, to ensure compatibility with braindecode.
+Mutually exclusive with return_epochs</p></li>
+<li><p><strong>cache_config</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><a class="reference internal" href="moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><em>CacheConfig</em></a>) – Configuration for caching of datasets. See <a class="reference internal" href="moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig" title="moabb.datasets.base.CacheConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.base.CacheConfig</span></code></a> for details.</p></li>
+<li><p><strong>postprocess_pipeline</strong> (<em>Pipeline</em><em> | </em><em>None</em>) – Optional pipeline to apply to the data after the preprocessing.
+This pipeline will either receive <a class="reference external" href="https://mne.tools/stable/generated/mne.io.BaseRaw.html#mne.io.BaseRaw" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.io.BaseRaw</span></code></a>, <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a>
+or <code class="xref py py-func docutils literal notranslate"><span class="pre">np.ndarray()</span></code> as input, depending on the values of <code class="docutils literal notranslate"><span class="pre">return_epochs</span></code>
+and <code class="docutils literal notranslate"><span class="pre">return_raws</span></code>.
+This pipeline must return an <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>.
+This pipeline must be “fixed” because it will not be trained,
+i.e. no call to <code class="docutils literal notranslate"><span class="pre">fit</span></code> will be made.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>X</strong> (<em>Union[np.ndarray, mne.Epochs]</em>) – the data that will be used as features for the model
+Note: if return_epochs=True,  this is mne.Epochs
+if return_epochs=False, this is np.ndarray</p></li>
+<li><p><strong>labels</strong> (<em>np.ndarray</em>) – the labels for training / evaluating the model</p></li>
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – A dataframe containing the metadata.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.is_valid">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L94-L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with the paradigm.</p>
+<p>This method is called to verify dataset is compatible with the
+paradigm.</p>
+<p>This method should raise an error if the dataset is not compatible
+with the paradigm. This is for example the case if the
+dataset is an ERP dataset for motor imagery paradigm, or if the
+dataset does not contain any of the required events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.make_labels_pipeline">
+<span class="sig-name descname"><span class="pre">make_labels_pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L195-L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.make_labels_pipeline" title="Permalink to this definition">#</a></dt>
+<dd><p>Returns the pipeline that extracts the labels from the
+output of the postprocess_pipeline.
+Refer to the arguments of <a class="reference internal" href="#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_data()</span></code></a> for more information.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.make_process_pipelines">
+<span class="sig-name descname"><span class="pre">make_process_pipelines</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_raws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">postprocess_pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L133-L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.make_process_pipelines" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the pre-processing pipelines corresponding to this paradigm (one per frequency band).
+Refer to the arguments of <a class="reference internal" href="#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_data()</span></code></a> for more information.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.match_all">
+<span class="sig-name descname"><span class="pre">match_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset"><span class="pre">BaseDataset</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shift</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_merge_strategy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'intersect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['stim']</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L434-L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.match_all" title="Permalink to this definition">#</a></dt>
+<dd><p>Initialize this paradigm to match all datasets in parameter:
+- <cite>self.resample</cite> is set to match the minimum frequency in all datasets, minus <cite>shift</cite>.</p>
+<blockquote>
+<div><p>If the frequency is 128 for example, then MNE can return 128 or 129 samples
+depending on the dataset, even if the length of the epochs is 1s
+Setting <cite>shift=-0.5</cite> solves this particular issue.</p>
+</div></blockquote>
+<ul class="simple">
+<li><p><cite>self.channels</cite> is initialized with the channels which are common to all datasets.</p></li>
+</ul>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>datasets</strong> (<em>List</em><em>[</em><a class="reference internal" href="moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset"><em>BaseDataset</em></a><em>]</em>) – A dataset instance.</p></li>
+<li><p><strong>shift</strong> (<em>List</em><em>[</em><a class="reference internal" href="moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset" title="moabb.datasets.base.BaseDataset"><em>BaseDataset</em></a><em>]</em>) – Shift the sampling frequency by this value
+E.g.: if sampling=128 and shift=-0.5, then it returns 127.5 Hz</p></li>
+<li><p><strong>channel_merge_strategy</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>default: 'intersect'</em><em>)</em>) – Accepts two values:
+- ‘intersect’: keep only channels common to all datasets
+- ‘union’: keep all channels from all datasets, removing duplicate</p></li>
+<li><p><strong>ignore</strong> (<em>List</em><em>[</em><em>string</em><em>]</em>) – A list of channels to ignore</p></li>
+<li><p><strong>..versionadded:</strong> – 0.6.0:</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.base.BaseProcessing.prepare_process">
+<span class="sig-name descname"><span class="pre">prepare_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/base.py#L113-L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.base.BaseProcessing.prepare_process" title="Permalink to this definition">#</a></dt>
+<dd><p>Prepare processing of raw files.</p>
+<p>This function allows to set parameter of the paradigm class prior to
+the preprocessing (process_raw). Does nothing by default and could be
+overloaded if needed.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset corresponding to the raw file. mainly use to access
+dataset specific information.</p>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.base.BaseProcessing.get_data"><code class="docutils literal notranslate"><span class="pre">BaseProcessing.get_data()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.base.BaseProcessing.is_valid"><code class="docutils literal notranslate"><span class="pre">BaseProcessing.is_valid()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.base.BaseProcessing.make_labels_pipeline"><code class="docutils literal notranslate"><span class="pre">BaseProcessing.make_labels_pipeline()</span></code></a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.base.BaseProcessing.match_all"><code class="docutils literal notranslate"><span class="pre">BaseProcessing.match_all()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.base.BaseProcessing.prepare_process"><code class="docutils literal notranslate"><span class="pre">BaseProcessing.prepare_process()</span></code></a></li>
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+<h1>moabb.paradigms.motor_imagery.BaseMotorImagery<a class="headerlink" href="#moabb-paradigms-motor-imagery-basemotorimagery" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.motor_imagery.BaseMotorImagery">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.motor_imagery.</span></span><span class="sig-name descname"><span class="pre">BaseMotorImagery</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">([7,</span> <span class="pre">35],)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L14-L105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.motor_imagery.BaseMotorImagery" title="Permalink to this definition">#</a></dt>
+<dd><p>Base Motor imagery paradigm.</p>
+<p>Please use one of the child classes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>defaults</em><em> [</em><em>[</em><em>7</em><em>, </em><em>35</em><em>]</em><em>]</em><em>)</em>) – bank of bandpass filter to apply.</p></li>
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – event to use for epoching. If None, default to all events defined in
+the dataset.</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.motor_imagery.BaseMotorImagery.datasets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that define the list of compatible datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.motor_imagery.BaseMotorImagery.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L76-L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.motor_imagery.BaseMotorImagery.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with the paradigm.</p>
+<p>This method is called to verify dataset is compatible with the
+paradigm.</p>
+<p>This method should raise an error if the dataset is not compatible
+with the paradigm. This is for example the case if the
+dataset is an ERP dataset for motor imagery paradigm, or if the
+dataset does not contain any of the required events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.motor_imagery.BaseMotorImagery.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.motor_imagery.BaseMotorImagery.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.motor_imagery.BaseMotorImagery"><code class="docutils literal notranslate"><span class="pre">BaseMotorImagery</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets"><code class="docutils literal notranslate"><span class="pre">BaseMotorImagery.datasets</span></code></a></li>
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+  <section id="moabb-paradigms-motor-imagery-filterbank">
+<h1>moabb.paradigms.motor_imagery.FilterBank<a class="headerlink" href="#moabb-paradigms-motor-imagery-filterbank" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.motor_imagery.FilterBank">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.motor_imagery.</span></span><span class="sig-name descname"><span class="pre">FilterBank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">([8,</span> <span class="pre">12],</span> <span class="pre">[12,</span> <span class="pre">16],</span> <span class="pre">[16,</span> <span class="pre">20],</span> <span class="pre">[20,</span> <span class="pre">24],</span> <span class="pre">[24,</span> <span class="pre">28],</span> <span class="pre">[28,</span> <span class="pre">32])</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L158-L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.motor_imagery.FilterBank" title="Permalink to this definition">#</a></dt>
+<dd><p>Filter Bank MI.</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+  <section id="moabb-paradigms-motor-imagery-singlepass">
+<h1>moabb.paradigms.motor_imagery.SinglePass<a class="headerlink" href="#moabb-paradigms-motor-imagery-singlepass" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.motor_imagery.SinglePass">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.motor_imagery.</span></span><span class="sig-name descname"><span class="pre">SinglePass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/motor_imagery.py#L108-L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.motor_imagery.SinglePass" title="Permalink to this definition">#</a></dt>
+<dd><p>Single Bandpass filter motor Imagery.</p>
+<p>Motor imagery paradigm with only one bandpass filter (default 8 to 32 Hz)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 8</em><em>)</em>) – cutoff frequency (Hz) for the high pass filter</p></li>
+<li><p><strong>fmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 32</em><em>)</em>) – cutoff frequency (Hz) for the low pass filter</p></li>
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – event to use for epoching. If None, default to all events defined in
+the dataset.</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-paradigms-motor-imagery-singlepass">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.paradigms.motor_imagery.SinglePass</span></code><a class="headerlink" href="#examples-using-moabb-paradigms-motor-imagery-singlepass" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="A paradigm defines how the raw data will be converted to trials ready to be processed by a deco..."><img alt="Explore Paradigm Object" src="../_images/sphx_glr_plot_explore_paradigm_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_explore_paradigm.html#sphx-glr-auto-examples-plot-explore-paradigm-py"><span class="std std-ref">Explore Paradigm Object</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Explore Paradigm Object</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to make a model selection in pipelines for finding the best model..."><img alt="GridSearch within a session" src="../_images/sphx_glr_plot_grid_search_withinsession_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_grid_search_withinsession.html#sphx-glr-auto-examples-advanced-examples-plot-grid-search-withinsession-py"><span class="std std-ref">GridSearch within a session</span></a></p>
+  <div class="sphx-glr-thumbnail-title">GridSearch within a session</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resa..."><img alt="Select Electrodes and Resampling" src="../_images/sphx_glr_plot_select_electrodes_resample_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_select_electrodes_resample.html#sphx-glr-auto-examples-advanced-examples-plot-select-electrodes-resample-py"><span class="std std-ref">Select Electrodes and Resampling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Select Electrodes and Resampling</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
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+  <section id="moabb-paradigms-p300-basep300">
+<h1>moabb.paradigms.p300.BaseP300<a class="headerlink" href="#moabb-paradigms-p300-basep300" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.p300.BaseP300">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.p300.</span></span><span class="sig-name descname"><span class="pre">BaseP300</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">([1,</span> <span class="pre">24],)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/p300.py#L14-L105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.p300.BaseP300" title="Permalink to this definition">#</a></dt>
+<dd><p>Base P300 paradigm.</p>
+<p>Please use one of the child classes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>defaults</em><em> [</em><em>[</em><em>7</em><em>, </em><em>35</em><em>]</em><em>]</em><em>)</em>) – bank of bandpass filter to apply.</p></li>
+<li><p><strong>events</strong> (<em>List</em><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – event to use for epoching. If None, default to all events defined in
+the dataset.</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – list of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.p300.BaseP300.datasets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.p300.BaseP300.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that define the list of compatible datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.p300.BaseP300.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/p300.py#L76-L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.p300.BaseP300.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Verify the dataset is compatible with the paradigm.</p>
+<p>This method is called to verify dataset is compatible with the
+paradigm.</p>
+<p>This method should raise an error if the dataset is not compatible
+with the paradigm. This is for example the case if the
+dataset is an ERP dataset for motor imagery paradigm, or if the
+dataset does not contain any of the required events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<em>dataset instance</em>) – The dataset to verify.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.p300.BaseP300.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.p300.BaseP300.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Property that defines scoring metric (e.g. ROC-AUC or accuracy
+or f-score), given as a sklearn-compatible string or a compatible
+sklearn scorer.</p>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.p300.BaseP300"><code class="docutils literal notranslate"><span class="pre">BaseP300</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.p300.BaseP300.datasets"><code class="docutils literal notranslate"><span class="pre">BaseP300.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.p300.BaseP300.is_valid"><code class="docutils literal notranslate"><span class="pre">BaseP300.is_valid()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.p300.BaseP300.scoring"><code class="docutils literal notranslate"><span class="pre">BaseP300.scoring</span></code></a></li>
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+                        Datasets
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+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="../dataset_summary.html">
+                        Datasets
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+                
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+  <section id="moabb-paradigms-ssvep-basessvep">
+<h1>moabb.paradigms.ssvep.BaseSSVEP<a class="headerlink" href="#moabb-paradigms-ssvep-basessvep" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.paradigms.ssvep.BaseSSVEP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.paradigms.ssvep.</span></span><span class="sig-name descname"><span class="pre">BaseSSVEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">((7,</span> <span class="pre">45),)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/ssvep.py#L13-L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.ssvep.BaseSSVEP" title="Permalink to this definition">#</a></dt>
+<dd><p>Base SSVEP Paradigm.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><em>None</em><em> (</em><em>default</em><em> [</em><em>7</em><em>, </em><em>45</em><em>]</em><em>)</em>) – Bank of bandpass filter to apply.</p></li>
+<li><p><strong>events</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of stimulation frequencies. If None, use all stimulus
+found in the dataset.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – Number of classes each dataset must have. All dataset classes if None.</p></li>
+<li><p><strong>tmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default 0.0</em><em>)</em>) – Start time (in second) of the epoch, relative to the dataset specific
+task interval e.g. tmin = 1 would mean the epoch will start 1 second
+after the beginning of the task as defined by the dataset.</p></li>
+<li><p><strong>tmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>(</em><em>default None</em><em>)</em>) – End time (in second) of the epoch, relative to the beginning of the
+dataset specific task interval. tmax = 5 would mean the epoch will end
+5 second after the beginning of the task as defined in the dataset. If
+None, use the dataset value.</p></li>
+<li><p><strong>baseline</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> of </em><em>length 2</em>) – The time interval to consider as “baseline” when applying baseline
+correction. If None, do not apply baseline correction.
+If a tuple (a, b), the interval is between a and b (in seconds),
+including the endpoints.
+Correction is applied by computing the mean of the baseline period
+and subtracting it from the data (see mne.Epochs)</p></li>
+<li><p><strong>channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – List of channel to select. If None, use all EEG channels available in
+the dataset.</p></li>
+<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em> (</em><em>default None</em><em>)</em>) – If not None, resample the eeg data with the sampling rate provided.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.ssvep.BaseSSVEP.datasets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">datasets</span></span><a class="headerlink" href="#moabb.paradigms.ssvep.BaseSSVEP.datasets" title="Permalink to this definition">#</a></dt>
+<dd><p>List of datasets valid for the paradigm.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.ssvep.BaseSSVEP.is_valid">
+<span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/ssvep.py#L89-L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.ssvep.BaseSSVEP.is_valid" title="Permalink to this definition">#</a></dt>
+<dd><p>Check if dataset is valid for the SSVEP paradigm.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.ssvep.BaseSSVEP.prepare_process">
+<span class="sig-name descname"><span class="pre">prepare_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/ssvep.py#L125-L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.ssvep.BaseSSVEP.prepare_process" title="Permalink to this definition">#</a></dt>
+<dd><p>Prepare dataset for processing, and using events if needed.</p>
+<p>This function is called before the processing function, and is used to
+prepare the dataset for processing. This includes:
+get the events used for the paradigm, and set the filters if needed.
+:param dataset: Dataset to prepare.
+:type dataset: moabb.datasets.base.BaseDataset</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="moabb.paradigms.ssvep.BaseSSVEP.scoring">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#moabb.paradigms.ssvep.BaseSSVEP.scoring" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the default scoring method for this paradigm.</p>
+<p>If n_classes use the roc_auc, else use accuracy. More details
+about this default scoring method can be found in the original
+moabb paper.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.paradigms.ssvep.BaseSSVEP.used_events">
+<span class="sig-name descname"><span class="pre">used_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/paradigms/ssvep.py#L102-L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.paradigms.ssvep.BaseSSVEP.used_events" title="Permalink to this definition">#</a></dt>
+<dd><p>Return the mne events used for the dataset.</p>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.ssvep.BaseSSVEP"><code class="docutils literal notranslate"><span class="pre">BaseSSVEP</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.ssvep.BaseSSVEP.datasets"><code class="docutils literal notranslate"><span class="pre">BaseSSVEP.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.ssvep.BaseSSVEP.is_valid"><code class="docutils literal notranslate"><span class="pre">BaseSSVEP.is_valid()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.ssvep.BaseSSVEP.prepare_process"><code class="docutils literal notranslate"><span class="pre">BaseSSVEP.prepare_process()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.ssvep.BaseSSVEP.scoring"><code class="docutils literal notranslate"><span class="pre">BaseSSVEP.scoring</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.paradigms.ssvep.BaseSSVEP.used_events"><code class="docutils literal notranslate"><span class="pre">BaseSSVEP.used_events()</span></code></a></li>
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+                  
+  <section id="moabb-pipelines-classification-ssvep-cca">
+<h1>moabb.pipelines.classification.SSVEP_CCA<a class="headerlink" href="#moabb-pipelines-classification-ssvep-cca" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_CCA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.classification.</span></span><span class="sig-name descname"><span class="pre">SSVEP_CCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_harmonics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L14-L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_CCA" title="Permalink to this definition">#</a></dt>
+<dd><p>Classifier based on Canonical Correlation Analysis for SSVEP.</p>
+<p>A CCA is computed from the set of training signals and some pure
+sinusoids to act as reference.
+Classification is made by taking the frequency with the max correlation,
+as proposed in <a class="reference internal" href="#rc3fd88100f85-1" id="id1">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>interval</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>length 2</em>) – List of form [tmin, tmax]. With tmin and tmax as defined in the SSVEP
+paradigm <a class="reference internal" href="moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP"><code class="xref py py-meth docutils literal notranslate"><span class="pre">moabb.paradigms.SSVEP()</span></code></a></p></li>
+<li><p><strong>freqs</strong> (<em>dict with n_classes keys</em>) – Frequencies corresponding to the SSVEP stimulation frequencies.
+They are used to identify SSVEP classes presents in the data.</p></li>
+<li><p><strong>n_harmonics</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of stimulation frequency’s harmonics to be used in the generation
+of the CCA reference signal.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc3fd88100f85-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Bin, G., Gao, X., Yan, Z., Hong, B., &amp; Gao, S. (2009). An online
+multi-channel SSVEP-based brain-computer interface using a
+canonical correlation analysis method. Journal of neural
+engineering, 6(4), 046002.
+<a class="reference external" href="https://doi.org/10.1088/1741-2560/6/4/046002">https://doi.org/10.1088/1741-2560/6/4/046002</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_CCA.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L60-L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_CCA.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>Compute reference sinusoid signal.</p>
+<p>These sinusoid are generated for each frequency in the dataset</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_CCA.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L81-L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_CCA.predict" title="Permalink to this definition">#</a></dt>
+<dd><p>Predict is made by taking the maximum correlation coefficient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_CCA.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L93-L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_CCA.predict_proba" title="Permalink to this definition">#</a></dt>
+<dd><p>Probability could be computed from the correlation coefficient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_CCA.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA"><span class="pre">SSVEP_CCA</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_CCA.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_CCA.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA"><span class="pre">SSVEP_CCA</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_CCA.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_CCA"><code class="docutils literal notranslate"><span class="pre">SSVEP_CCA</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_CCA.fit"><code class="docutils literal notranslate"><span class="pre">SSVEP_CCA.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_CCA.predict"><code class="docutils literal notranslate"><span class="pre">SSVEP_CCA.predict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_CCA.predict_proba"><code class="docutils literal notranslate"><span class="pre">SSVEP_CCA.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_CCA.set_fit_request"><code class="docutils literal notranslate"><span class="pre">SSVEP_CCA.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_CCA.set_score_request"><code class="docutils literal notranslate"><span class="pre">SSVEP_CCA.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-classification-ssvep-msetcca">
+<h1>moabb.pipelines.classification.SSVEP_MsetCCA<a class="headerlink" href="#moabb-pipelines-classification-ssvep-msetcca" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_MsetCCA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.classification.</span></span><span class="sig-name descname"><span class="pre">SSVEP_MsetCCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">freqs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L556-L665"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_MsetCCA" title="Permalink to this definition">#</a></dt>
+<dd><p>Classifier based on MsetCCA for SSVEP.</p>
+<blockquote>
+<div><p>The MsetCCA method learns multiple linear transforms to extract
+SSVEP common features from multiple sets of EEG data. These are then used
+to compute the reference signal used in CCA <a class="reference internal" href="#r59288fba6272-1" id="id1">[1]</a>.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>freqs</strong> (<em>dict with n_classes keys</em>) – Frequencies corresponding to the SSVEP stimulation frequencies.
+They are used to identify SSVEP classes presents in the data.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of multisets spatial filters used per sample data.
+It corresponds to the number of eigen vectors taken the solution of the
+MAXVAR objective function as formulated in Eq.5 in <a class="reference internal" href="#r59288fba6272-1" id="id2">[1]</a>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r59288fba6272-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>)</span>
+<p>Zhang, Y.U., Zhou, G., Jin, J., Wang, X. and Cichocki, A. (2014). Frequency
+recognition in SSVEP-based BCI using multiset canonical correlation analysis.
+International journal of neural systems, 24(04), p.1450013.
+<a class="reference external" href="https://doi.org/10.1142/S0129065714500130">https://doi.org/10.1142/S0129065714500130</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_MsetCCA.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L594-L637"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_MsetCCA.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>Compute the optimized reference signal at each stimulus
+frequency.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_MsetCCA.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L639-L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_MsetCCA.predict" title="Permalink to this definition">#</a></dt>
+<dd><p>Predict is made by taking the maximum correlation coefficient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_MsetCCA.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L654-L665"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_MsetCCA.predict_proba" title="Permalink to this definition">#</a></dt>
+<dd><p>Probability could be computed from the correlation coefficient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_MsetCCA.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.classification.SSVEP_MsetCCA" title="moabb.pipelines.classification.SSVEP_MsetCCA"><span class="pre">SSVEP_MsetCCA</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_MsetCCA.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_MsetCCA.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.classification.SSVEP_MsetCCA" title="moabb.pipelines.classification.SSVEP_MsetCCA"><span class="pre">SSVEP_MsetCCA</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_MsetCCA.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_MsetCCA"><code class="docutils literal notranslate"><span class="pre">SSVEP_MsetCCA</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_MsetCCA.fit"><code class="docutils literal notranslate"><span class="pre">SSVEP_MsetCCA.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_MsetCCA.predict"><code class="docutils literal notranslate"><span class="pre">SSVEP_MsetCCA.predict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_MsetCCA.predict_proba"><code class="docutils literal notranslate"><span class="pre">SSVEP_MsetCCA.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_MsetCCA.set_fit_request"><code class="docutils literal notranslate"><span class="pre">SSVEP_MsetCCA.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_MsetCCA.set_score_request"><code class="docutils literal notranslate"><span class="pre">SSVEP_MsetCCA.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-classification-ssvep-trca">
+<h1>moabb.pipelines.classification.SSVEP_TRCA<a class="headerlink" href="#moabb-pipelines-classification-ssvep-trca" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.classification.</span></span><span class="sig-name descname"><span class="pre">SSVEP_TRCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">downsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_ensemble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'original'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scm'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L104-L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA" title="Permalink to this definition">#</a></dt>
+<dd><p>Classifier based on the Task-Related Component Analysis method <a href="#id6"><span class="problematic" id="id1">[1]_</span></a> for
+SSVEP.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the data to be analyzed.</p></li>
+<li><p><strong>freqs</strong> (<em>dict with n_classes keys</em>) – Frequencies corresponding to the SSVEP components. These are
+necessary to design the filterbank bands.</p></li>
+<li><p><strong>downsample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>default=1</em>) – Factor by which downsample the data. A downsample value of N will result
+on a sampling frequency of (sfreq // N) by taking one sample every N of
+the original data. In the original TRCA paper <a href="#id7"><span class="problematic" id="id2">[1]_</span></a> data are at 250Hz.</p></li>
+<li><p><strong>is_ensemble</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>default=False</em>) – If True, predict on new data using the Ensemble-TRCA method described
+in <a href="#id8"><span class="problematic" id="id3">[1]_</span></a>.</p></li>
+<li><p><strong>method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>default='original'</em>) – ‘original’ computes euclidean mean for S as in the original paper <a href="#id9"><span class="problematic" id="id4">[1]_</span></a>.
+‘riemann’ variation computes geodesic mean instead. This geodesic
+mean is more robust to outlier but negatively impacted by ill-conditioned
+matrices (when only few samples are available for training for instance).
+If the geometric mean can’t be estimated, please consider trying ‘logeuclid’.
+It computes log-euclidean mean instead of the affine-invariant one and is more robust
+computationally.
+‘riemann’ and ‘logeuclid’ variations are useful when lots of noisy
+training data are available. With few training data ‘original’ is more
+appropriate.</p></li>
+<li><p><strong>estimator</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – For both methods, regularization to use for covariance matrices estimations.
+Consider ‘schaefer’, ‘lwf’, ‘oas’ or ‘scm’ for no regularization.
+In the original implementation from TRCA paper <a href="#id10"><span class="problematic" id="id5">[1]_</span></a>, no regularization
+is used. So method=’original’ and regul=’scm’ is similar to original
+implementation.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.fb_coefs">
+<span class="sig-name descname"><span class="pre">fb_coefs</span></span><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.fb_coefs" title="Permalink to this definition">#</a></dt>
+<dd><p>Alpha coefficients for the fusion of the filterbank sub-bands.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of len (n_fbands)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.classes_">
+<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.classes_" title="Permalink to this definition">#</a></dt>
+<dd><p>Array with the class labels extracted at fit time.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_classes,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.n_classes">
+<span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.n_classes" title="Permalink to this definition">#</a></dt>
+<dd><p>Number of unique labels/classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.templates_">
+<span class="sig-name descname"><span class="pre">templates_</span></span><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.templates_" title="Permalink to this definition">#</a></dt>
+<dd><p>Template data obtained by averaging all training trials for a given
+class. Each class templates is divided in n_fbands sub-bands extracted
+from the filterbank approach.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_classes, n_bands, n_channels, n_samples)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.weights_">
+<span class="sig-name descname"><span class="pre">weights_</span></span><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.weights_" title="Permalink to this definition">#</a></dt>
+<dd><p>Weight coefficients for the different electrodes which are used
+as spatial filters for the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_fbands, n_classes, n_channels)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.Reference">
+<span class="sig-name descname"><span class="pre">Reference</span></span><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.Reference" title="Permalink to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">----------</span></span></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">..</span> <span class="pre">[1]</span> <span class="pre">M.</span> <span class="pre">Nakanishi,</span> <span class="pre">Y.</span> <span class="pre">Wang,</span> <span class="pre">X.</span> <span class="pre">Chen,</span> <span class="pre">Y.</span> <span class="pre">-T.</span> <span class="pre">Wang,</span> <span class="pre">X.</span> <span class="pre">Gao,</span> <span class="pre">and</span> <span class="pre">T.-P.</span> <span class="pre">Jung,</span></span></dt>
+<dd><p>“Enhancing detection of SSVEPs for a high-speed brain speller using
+task-related component analysis”,
+IEEE Trans. Biomed. Eng, 65(1):104-112, 2018.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">Code</span> <span class="pre">based</span> <span class="pre">on</span> <span class="pre">the</span> <span class="pre">Matlab</span> <span class="pre">implementation</span> <span class="pre">from</span> <span class="pre">authors</span> <span class="pre">of</span> <span class="pre">[1]_</span></span></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">(https</span></span></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>//github.com/mnakanishi/TRCA-SSVEP).</p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.4.4.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L326-L373"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>Extract spatial filters and templates from the given calibration
+data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>ndarray</em><em> of </em><em>shape</em><em> (</em><em>n_trials</em><em>, </em><em>n_channels</em><em>, </em><em>n_samples</em><em>)</em>) – Training data. Trials are grouped by class, divided in n_fbands bands by
+the filterbank approach and then used to calculate weight vectors and
+templates for each class and band.</p></li>
+<li><p><strong>y</strong> (<em>ndarray</em><em> of </em><em>shape</em><em> (</em><em>n_trials</em><em>,</em><em>)</em>) – Label vector in respect to X.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Instance of classifier.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>CCA object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L375-L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.predict" title="Permalink to this definition">#</a></dt>
+<dd><p>Make predictions on unseen data.</p>
+<p>The new data observation X will be filtered
+with weights previously extracted and compared to the templates to assess
+similarity with each of them and select a class based on the maximal value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>ndarray</em><em> of </em><em>shape</em><em> (</em><em>n_trials</em><em>, </em><em>n_channels</em><em>, </em><em>n_samples</em><em>)</em>) – Testing data. This will be divided in self.n_fbands using the filter- bank approach,
+then it will be transformed by the different spatial filters and compared to the
+previously fit templates according to the selected method for analysis (ensemble or
+not). Finally, correlation scores for all sub-bands of each class will be combined,
+resulting on a single correlation score per class, from which the maximal one is
+identified as the predicted class of the data.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_pred</strong> – Prediction vector in respect to X.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_trials,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/classification.py#L455-L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.predict_proba" title="Permalink to this definition">#</a></dt>
+<dd><p>Make predictions on unseen data with the associated probabilities.</p>
+<p>The new data observation X will be filtered
+with weights previously extracted and compared to the templates to assess
+similarity with each of them and select a class based on the maximal value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>ndarray</em><em> of </em><em>shape</em><em> (</em><em>n_trials</em><em>, </em><em>n_channels</em><em>, </em><em>n_samples</em><em>)</em>) – Testing data. This will be divided in self.n_fbands using the filter-bank approach,
+then it will be transformed by the different spatial filters and compared to the
+previously fit templates according to the selected method for analysis (ensemble or
+not). Finally, correlation scores for all sub-bands of each class will be combined,
+resulting on a single correlation score per class, from which the maximal one is
+identified as the predicted class of the data.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_pred</strong> – Prediction vector in respect to X.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_trials,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.classification.SSVEP_TRCA.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.classification.SSVEP_TRCA" title="moabb.pipelines.classification.SSVEP_TRCA"><span class="pre">SSVEP_TRCA</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.classification.SSVEP_TRCA.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
+                </article>
+              
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.fb_coefs"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.fb_coefs</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.classes_"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.classes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.n_classes"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.n_classes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.templates_"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.templates_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.weights_"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.weights_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.Reference"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.Reference</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.fit"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.predict"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.predict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.predict_proba"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.classification.SSVEP_TRCA.set_score_request"><code class="docutils literal notranslate"><span class="pre">SSVEP_TRCA.set_score_request()</span></code></a></li>
+</ul>
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+<h1>moabb.pipelines.csp.TRCSP<a class="headerlink" href="#moabb-pipelines-csp-trcsp" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.csp.TRCSP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.csp.</span></span><span class="sig-name descname"><span class="pre">TRCSP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nfilter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/csp.py#L7-L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.csp.TRCSP" title="Permalink to this definition">#</a></dt>
+<dd><p>Weighted Tikhonov-regularized CSP as described in Lotte and Guan
+2011.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.csp.TRCSP.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/csp.py#L15-L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.csp.TRCSP.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>Train spatial filters.</p>
+<p>Only deals with two class</p>
+</dd></dl>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.csp.TRCSP.fit"><code class="docutils literal notranslate"><span class="pre">TRCSP.fit()</span></code></a></li>
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diff --git a/docs/generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html b/docs/generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html
new file mode 100644
index 00000000..580428cb
--- /dev/null
+++ b/docs/generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html
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+  <section id="moabb-pipelines-deep-learning-kerasdeepconvnet">
+<h1>moabb.pipelines.deep_learning.KerasDeepConvNet<a class="headerlink" href="#moabb-pipelines-deep-learning-kerasdeepconvnet" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasDeepConvNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.deep_learning.</span></span><span class="sig-name descname"><span class="pre">KerasDeepConvNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/deep_learning.py#L150-L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasDeepConvNet" title="Permalink to this definition">#</a></dt>
+<dd><p>Keras implementation of the Deep Convolutional Network as described in
+<a class="reference internal" href="#r679315cfbef6-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by the Army Research Laboratory (ARL)
+at <a class="github reference external" href="https://github.com/vlawhern/arl-eegmodels">vlawhern/arl-eegmodels</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r679315cfbef6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger,
+K., Tangermann, M., … &amp; Ball, T. (2017). Deep learning with convolutional neural networks
+for EEG decoding and visualization. Human brain mapping, 38(11), 5391-5420.
+<a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasDeepConvNet.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasDeepConvNet" title="moabb.pipelines.deep_learning.KerasDeepConvNet"><span class="pre">KerasDeepConvNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasDeepConvNet.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasDeepConvNet.set_partial_fit_request">
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasDeepConvNet" title="moabb.pipelines.deep_learning.KerasDeepConvNet"><span class="pre">KerasDeepConvNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasDeepConvNet.set_partial_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasDeepConvNet.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasDeepConvNet" title="moabb.pipelines.deep_learning.KerasDeepConvNet"><span class="pre">KerasDeepConvNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasDeepConvNet.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasDeepConvNet"><code class="docutils literal notranslate"><span class="pre">KerasDeepConvNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasDeepConvNet.set_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasDeepConvNet.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasDeepConvNet.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasDeepConvNet.set_partial_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasDeepConvNet.set_score_request"><code class="docutils literal notranslate"><span class="pre">KerasDeepConvNet.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-deep-learning-keraseegitnet">
+<h1>moabb.pipelines.deep_learning.KerasEEGITNet<a class="headerlink" href="#moabb-pipelines-deep-learning-keraseegitnet" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGITNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.deep_learning.</span></span><span class="sig-name descname"><span class="pre">KerasEEGITNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/deep_learning.py#L579-L797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGITNet" title="Permalink to this definition">#</a></dt>
+<dd><p>Keras implementation of the EEGITNet as described in <a class="reference internal" href="#rf5b2ee1af1ae-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by
+at <a class="github reference external" href="https://github.com/AbbasSalami/EEG-ITNet">AbbasSalami/EEG-ITNet</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rf5b2ee1af1ae-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Salami, A., Andreu-Perez, J., &amp; Gillmeister, H. (2022). EEG-ITNet: An explainable inception temporal
+convolutional network for motor imagery classification. IEEE Access, 10, 36672-36685.
+<a class="reference external" href="https://doi.org/10.1109/ACCESS.2022.3161489">https://doi.org/10.1109/ACCESS.2022.3161489</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGITNet.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGITNet" title="moabb.pipelines.deep_learning.KerasEEGITNet"><span class="pre">KerasEEGITNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGITNet.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGITNet.set_partial_fit_request">
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGITNet" title="moabb.pipelines.deep_learning.KerasEEGITNet"><span class="pre">KerasEEGITNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGITNet.set_partial_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGITNet.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGITNet" title="moabb.pipelines.deep_learning.KerasEEGITNet"><span class="pre">KerasEEGITNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGITNet.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGITNet"><code class="docutils literal notranslate"><span class="pre">KerasEEGITNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGITNet.set_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGITNet.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGITNet.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGITNet.set_partial_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGITNet.set_score_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGITNet.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-deep-learning-keraseegnex">
+<h1>moabb.pipelines.deep_learning.KerasEEGNeX<a class="headerlink" href="#moabb-pipelines-deep-learning-keraseegnex" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNeX">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.deep_learning.</span></span><span class="sig-name descname"><span class="pre">KerasEEGNeX</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/deep_learning.py#L438-L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNeX" title="Permalink to this definition">#</a></dt>
+<dd><p>Keras implementation of the EEGNex as described in <a class="reference internal" href="#r643fa75c3283-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by
+at <a class="github reference external" href="https://github.com/chenxiachan/EEGNeX">chenxiachan/EEGNeX</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r643fa75c3283-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Chen, X., Teng, X., Chen, H., Pan, Y., &amp; Geyer, P. (2022). Toward reliable signals decoding for
+electroencephalogram: A benchmark study to EEGNeX. arXiv preprint arXiv:2207.12369.
+https://doi.org/10.48550/arXiv.2207.12369</p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNeX.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGNeX" title="moabb.pipelines.deep_learning.KerasEEGNeX"><span class="pre">KerasEEGNeX</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNeX.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNeX.set_partial_fit_request">
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGNeX" title="moabb.pipelines.deep_learning.KerasEEGNeX"><span class="pre">KerasEEGNeX</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNeX.set_partial_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNeX.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGNeX" title="moabb.pipelines.deep_learning.KerasEEGNeX"><span class="pre">KerasEEGNeX</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNeX.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNeX"><code class="docutils literal notranslate"><span class="pre">KerasEEGNeX</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNeX.set_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGNeX.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNeX.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGNeX.set_partial_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNeX.set_score_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGNeX.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-deep-learning-keraseegnet-8-2">
+<h1>moabb.pipelines.deep_learning.KerasEEGNet_8_2<a class="headerlink" href="#moabb-pipelines-deep-learning-keraseegnet-8-2" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNet_8_2">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.deep_learning.</span></span><span class="sig-name descname"><span class="pre">KerasEEGNet_8_2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/deep_learning.py#L259-L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2" title="Permalink to this definition">#</a></dt>
+<dd><p>Keras implementation of the EEGNet as described in <a class="reference internal" href="#rd83becb56589-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by the Army Research Laboratory (ARL)
+at <a class="github reference external" href="https://github.com/vlawhern/arl-eegmodels">vlawhern/arl-eegmodels</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd83becb56589-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., &amp; Lance, B. J. (2018). EEGNet:
+a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of neural
+engineering, 15(5), 056013.
+<a class="reference external" href="https://doi.org/10.1088/1741-2552/aace8c">https://doi.org/10.1088/1741-2552/aace8c</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2" title="moabb.pipelines.deep_learning.KerasEEGNet_8_2"><span class="pre">KerasEEGNet_8_2</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_partial_fit_request">
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2" title="moabb.pipelines.deep_learning.KerasEEGNet_8_2"><span class="pre">KerasEEGNet_8_2</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_partial_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2" title="moabb.pipelines.deep_learning.KerasEEGNet_8_2"><span class="pre">KerasEEGNet_8_2</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2"><code class="docutils literal notranslate"><span class="pre">KerasEEGNet_8_2</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGNet_8_2.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGNet_8_2.set_partial_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_score_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGNet_8_2.set_score_request()</span></code></a></li>
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new file mode 100644
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+  <section id="moabb-pipelines-deep-learning-keraseegtcnet">
+<h1>moabb.pipelines.deep_learning.KerasEEGTCNet<a class="headerlink" href="#moabb-pipelines-deep-learning-keraseegtcnet" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGTCNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.deep_learning.</span></span><span class="sig-name descname"><span class="pre">KerasEEGTCNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/deep_learning.py#L344-L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGTCNet" title="Permalink to this definition">#</a></dt>
+<dd><p>Keras implementation of the EEGTCNet as described in <a class="reference internal" href="#r89b58824c471-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by
+at <a class="github reference external" href="https://github.com/iis-eth-zurich/eeg-tcnet">iis-eth-zurich/eeg-tcnet</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r89b58824c471-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., &amp; Benini, L. (2020, October).
+EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain–machine interfaces.
+In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2958-2965). IEEE.
+<a class="reference external" href="https://doi.org/10.1109/SMC42975.2020.9283028">https://doi.org/10.1109/SMC42975.2020.9283028</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGTCNet.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGTCNet" title="moabb.pipelines.deep_learning.KerasEEGTCNet"><span class="pre">KerasEEGTCNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGTCNet.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGTCNet.set_partial_fit_request">
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGTCNet" title="moabb.pipelines.deep_learning.KerasEEGTCNet"><span class="pre">KerasEEGTCNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGTCNet.set_partial_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasEEGTCNet.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasEEGTCNet" title="moabb.pipelines.deep_learning.KerasEEGTCNet"><span class="pre">KerasEEGTCNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasEEGTCNet.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGTCNet"><code class="docutils literal notranslate"><span class="pre">KerasEEGTCNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGTCNet.set_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGTCNet.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGTCNet.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGTCNet.set_partial_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasEEGTCNet.set_score_request"><code class="docutils literal notranslate"><span class="pre">KerasEEGTCNet.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-deep-learning-kerasshallowconvnet">
+<h1>moabb.pipelines.deep_learning.KerasShallowConvNet<a class="headerlink" href="#moabb-pipelines-deep-learning-kerasshallowconvnet" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasShallowConvNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.deep_learning.</span></span><span class="sig-name descname"><span class="pre">KerasShallowConvNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/deep_learning.py#L65-L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasShallowConvNet" title="Permalink to this definition">#</a></dt>
+<dd><p>Keras implementation of the Shallow Convolutional Network as described
+in <a class="reference internal" href="#r2ccacb732305-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by the Army Research Laboratory (ARL)
+at <a class="github reference external" href="https://github.com/vlawhern/arl-eegmodels">vlawhern/arl-eegmodels</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2ccacb732305-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger,
+K., Tangermann, M., … &amp; Ball, T. (2017). Deep learning with convolutional neural networks
+for EEG decoding and visualization. Human brain mapping, 38(11), 5391-5420.
+<a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+<p class="rubric">Notes</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.5.0.</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasShallowConvNet.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasShallowConvNet" title="moabb.pipelines.deep_learning.KerasShallowConvNet"><span class="pre">KerasShallowConvNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasShallowConvNet.set_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasShallowConvNet.set_partial_fit_request">
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasShallowConvNet" title="moabb.pipelines.deep_learning.KerasShallowConvNet"><span class="pre">KerasShallowConvNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasShallowConvNet.set_partial_fit_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+<li><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.deep_learning.KerasShallowConvNet.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#moabb.pipelines.deep_learning.KerasShallowConvNet" title="moabb.pipelines.deep_learning.KerasShallowConvNet"><span class="pre">KerasShallowConvNet</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_metadata_requests.py#L1033-L1060"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.deep_learning.KerasShallowConvNet.set_score_request" title="Permalink to this definition">#</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
+Please see <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.4)"><span class="xref std std-ref">User Guide</span></a> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasShallowConvNet"><code class="docutils literal notranslate"><span class="pre">KerasShallowConvNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasShallowConvNet.set_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasShallowConvNet.set_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasShallowConvNet.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">KerasShallowConvNet.set_partial_fit_request()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.deep_learning.KerasShallowConvNet.set_score_request"><code class="docutils literal notranslate"><span class="pre">KerasShallowConvNet.set_score_request()</span></code></a></li>
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+  <section id="moabb-pipelines-features-augmenteddataset">
+<h1>moabb.pipelines.features.AugmentedDataset<a class="headerlink" href="#moabb-pipelines-features-augmenteddataset" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.features.AugmentedDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.features.</span></span><span class="sig-name descname"><span class="pre">AugmentedDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L80-L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.AugmentedDataset" title="Permalink to this definition">#</a></dt>
+<dd><p>Dataset augmentation methods in a higher dimensional space.</p>
+<p>This transformation allow to create an embedding version of the current
+dataset. The implementation and the application is described in <a class="reference internal" href="#r5c84a0c24d22-1" id="id1">[1]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r5c84a0c24d22-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Carrara, I., &amp; Papadopoulo, T. (2023). Classification of BCI-EEG based on augmented covariance matrix.
+arXiv preprint arXiv:2302.04508.
+https://doi.org/10.48550/arXiv.2302.04508</p>
+</div>
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+  <section id="moabb-pipelines-features-extendedssvepsignal">
+<h1>moabb.pipelines.features.ExtendedSSVEPSignal<a class="headerlink" href="#moabb-pipelines-features-extendedssvepsignal" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.features.ExtendedSSVEPSignal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.features.</span></span><span class="sig-name descname"><span class="pre">ExtendedSSVEPSignal</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L51-L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.ExtendedSSVEPSignal" title="Permalink to this definition">#</a></dt>
+<dd><p>Prepare FilterBank SSVEP EEG signal for estimating extended covariances.</p>
+<p>Riemannian approaches on SSVEP rely on extended covariances
+matrices, where the filtered signals are contenated to estimate a
+large covariance matrice.</p>
+<p>FilterBank SSVEP EEG are of shape (n_trials, n_channels, n_times,
+n_freqs) and should be convert in (n_trials, n_channels*n_freqs,
+n_times) to estimate covariance matrices of (n_channels*n_freqs,
+n_channels*n_freqs).</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.features.ExtendedSSVEPSignal.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L68-L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.ExtendedSSVEPSignal.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>No need to fit for ExtendedSSVEPSignal.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.features.ExtendedSSVEPSignal.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_set_output.py#L72-L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.ExtendedSSVEPSignal.transform" title="Permalink to this definition">#</a></dt>
+<dd><p>Transpose and reshape EEG for extended covmat estimation.</p>
+</dd></dl>
+
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.ExtendedSSVEPSignal"><code class="docutils literal notranslate"><span class="pre">ExtendedSSVEPSignal</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.ExtendedSSVEPSignal.fit"><code class="docutils literal notranslate"><span class="pre">ExtendedSSVEPSignal.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.ExtendedSSVEPSignal.transform"><code class="docutils literal notranslate"><span class="pre">ExtendedSSVEPSignal.transform()</span></code></a></li>
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+  <section id="moabb-pipelines-features-fm">
+<h1>moabb.pipelines.features.FM<a class="headerlink" href="#moabb-pipelines-features-fm" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.features.FM">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.features.</span></span><span class="sig-name descname"><span class="pre">FM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L22-L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.FM" title="Permalink to this definition">#</a></dt>
+<dd><p>Transformer to scale sampling frequency.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.features.FM.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L41-L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.FM.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>Only for scikit-learn compatibility.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.features.FM.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_set_output.py#L45-L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.FM.transform" title="Permalink to this definition">#</a></dt>
+<dd><p>transform.</p>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.FM.fit"><code class="docutils literal notranslate"><span class="pre">FM.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.FM.transform"><code class="docutils literal notranslate"><span class="pre">FM.transform()</span></code></a></li>
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diff --git a/docs/generated/moabb.pipelines.features.LogVariance.html b/docs/generated/moabb.pipelines.features.LogVariance.html
new file mode 100644
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+<h1>moabb.pipelines.features.LogVariance<a class="headerlink" href="#moabb-pipelines-features-logvariance" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.features.LogVariance">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.features.</span></span><span class="sig-name descname"><span class="pre">LogVariance</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L9-L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.LogVariance" title="Permalink to this definition">#</a></dt>
+<dd><p>LogVariance transformer.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.features.LogVariance.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L12-L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.LogVariance.fit" title="Permalink to this definition">#</a></dt>
+<dd><p>fit.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="moabb.pipelines.features.LogVariance.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/../.venv/lib/python3.9/site-packages/sklearn/utils/_set_output.py#L16-L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.LogVariance.transform" title="Permalink to this definition">#</a></dt>
+<dd><p>transform.</p>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.LogVariance.fit"><code class="docutils literal notranslate"><span class="pre">LogVariance.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#moabb.pipelines.features.LogVariance.transform"><code class="docutils literal notranslate"><span class="pre">LogVariance.transform()</span></code></a></li>
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diff --git a/docs/generated/moabb.pipelines.features.StandardScaler_Epoch.html b/docs/generated/moabb.pipelines.features.StandardScaler_Epoch.html
new file mode 100644
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+<h1>moabb.pipelines.features.StandardScaler_Epoch<a class="headerlink" href="#moabb-pipelines-features-standardscaler-epoch" title="Permalink to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="moabb.pipelines.features.StandardScaler_Epoch">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">moabb.pipelines.features.</span></span><span class="sig-name descname"><span class="pre">StandardScaler_Epoch</span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/features.py#L115-L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.features.StandardScaler_Epoch" title="Permalink to this definition">#</a></dt>
+<dd><p>Function to standardize the X raw data for the DeepLearning Method.</p>
+</dd></dl>
+
+<section id="examples-using-moabb-pipelines-features-standardscaler-epoch">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.pipelines.features.StandardScaler_Epoch</span></code><a class="headerlink" href="#examples-using-moabb-pipelines-features-standardscaler-epoch" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use load the pretrained pipeline in MOABB."><img alt="Load Model (Scikit, Pytorch, Keras) with MOABB" src="../_images/sphx_glr_load_model_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/load_model.html#sphx-glr-auto-examples-load-model-py"><span class="std std-ref">Load Model (Scikit, Pytorch, Keras) with MOABB</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load Model (Scikit, Pytorch, Keras) with MOABB</div>
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diff --git a/docs/generated/moabb.pipelines.utils.FilterBank.html b/docs/generated/moabb.pipelines.utils.FilterBank.html
new file mode 100644
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+<h1>moabb.pipelines.utils.FilterBank<a class="headerlink" href="#moabb-pipelines-utils-filterbank" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.pipelines.utils.FilterBank">
+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils.</span></span><span class="sig-name descname"><span class="pre">FilterBank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils.py#L209-L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils.FilterBank" title="Permalink to this definition">#</a></dt>
+<dd><p>Apply a given identical pipeline over a bank of filter.</p>
+<p>The pipeline provided with the constrictor will be appield on the 4th
+axis of the input data. This pipeline should be used with a FilterBank
+paradigm.</p>
+<p>This can be used to build a filterbank CSP, for example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pipeline</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">FilterBank</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">CSP</span><span class="p">()),</span> <span class="n">LDA</span><span class="p">())</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>estimator</strong> (<em>sklean Estimator</em>) – the sklearn pipeline to apply on each band of the filter bank.</p></li>
+<li><p><strong>flatten</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>True</em><em>)</em>) – If True, output of each band are concatenated together on the feature
+axis. if False, output are stacked.</p></li>
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+<h1>moabb.pipelines.utils.create_pipeline_from_config<a class="headerlink" href="#moabb-pipelines-utils-create-pipeline-from-config" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.pipelines.utils.create_pipeline_from_config">
+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils.</span></span><span class="sig-name descname"><span class="pre">create_pipeline_from_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils.py#L20-L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils.create_pipeline_from_config" title="Permalink to this definition">#</a></dt>
+<dd><p>Create a pipeline from a config file.</p>
+<p>takes a config dict as input and return the corresponding pipeline.</p>
+<p>If the pipeline is a Tensorflow pipeline it convert also the optimizer function and the callbacks.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>config</strong> (<em>Dict.</em>) – Dict containing the config parameters.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>pipeline</strong> – sklearn Pipeline</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Pipeline</p>
+</dd>
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diff --git a/docs/generated/moabb.pipelines.utils_deep_model.EEGNet.html b/docs/generated/moabb.pipelines.utils_deep_model.EEGNet.html
new file mode 100644
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+  <section id="moabb-pipelines-utils-deep-model-eegnet">
+<h1>moabb.pipelines.utils_deep_model.EEGNet<a class="headerlink" href="#moabb-pipelines-utils-deep-model-eegnet" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.pipelines.utils_deep_model.EEGNet">
+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils_deep_model.</span></span><span class="sig-name descname"><span class="pre">EEGNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filters_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'elu'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils_deep_model.py#L26-L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils_deep_model.EEGNet" title="Permalink to this definition">#</a></dt>
+<dd><p>EEGNet block implementation as described in <a class="reference internal" href="#r820c2366bc63-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by The Integrated Systems Laboratory of ETH Zurich
+at <a class="github reference external" href="https://github.com/iis-eth-zurich/eeg-tcnet">iis-eth-zurich/eeg-tcnet</a></p>
+<p>We use the original parameter implemented on the paper.</p>
+<p>Note that this implementation has not been verified by the original
+authors.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r820c2366bc63-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., &amp; Lance, B. J. (2018). EEGNet:
+a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of neural
+engineering, 15(5), 056013.
+<a class="reference external" href="https://doi.org/10.1088/1741-2552/aace8c">https://doi.org/10.1088/1741-2552/aace8c</a></p>
+</div>
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+  <section id="moabb-pipelines-utils-deep-model-eegnet-tc">
+<h1>moabb.pipelines.utils_deep_model.EEGNet_TC<a class="headerlink" href="#moabb-pipelines-utils-deep-model-eegnet-tc" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.pipelines.utils_deep_model.EEGNet_TC">
+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils_deep_model.</span></span><span class="sig-name descname"><span class="pre">EEGNet_TC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernLength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'elu'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils_deep_model.py#L153-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils_deep_model.EEGNet_TC" title="Permalink to this definition">#</a></dt>
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diff --git a/docs/generated/moabb.pipelines.utils_deep_model.TCN_block.html b/docs/generated/moabb.pipelines.utils_deep_model.TCN_block.html
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+<h1>moabb.pipelines.utils_deep_model.TCN_block<a class="headerlink" href="#moabb-pipelines-utils-deep-model-tcn-block" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.pipelines.utils_deep_model.TCN_block">
+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils_deep_model.</span></span><span class="sig-name descname"><span class="pre">TCN_block</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_dimension</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils_deep_model.py#L79-L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils_deep_model.TCN_block" title="Permalink to this definition">#</a></dt>
+<dd><p>Temporal Convolutional Network (TCN), TCN_block from <a class="reference internal" href="#r2eea69aed7b6-1" id="id1">[1]</a>.</p>
+<p>This implementation is taken from code by The Integrated Systems Laboratory of ETH Zurich
+at <a class="github reference external" href="https://github.com/iis-eth-zurich/eeg-tcnet">iis-eth-zurich/eeg-tcnet</a></p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2eea69aed7b6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., &amp; Benini, L. (2020, October).
+EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain–machine interfaces.
+In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2958-2965). IEEE.
+<a class="reference external" href="https://doi.org/10.48550/arXiv.2006.00622">https://doi.org/10.48550/arXiv.2006.00622</a></p>
+</div>
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diff --git a/docs/generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html b/docs/generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html
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+<h1>moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader<a class="headerlink" href="#moabb-pipelines-utils-pytorch-braindecodedatasetloader" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
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+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils_pytorch.</span></span><span class="sig-name descname"><span class="pre">BraindecodeDatasetLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kw_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils_pytorch.py#L34-L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader" title="Permalink to this definition">#</a></dt>
+<dd><p>Class to Load the data from MOABB in a format compatible with
+braindecode.</p>
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diff --git a/docs/generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html b/docs/generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html
new file mode 100644
index 00000000..e016dc2e
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+  <section id="moabb-pipelines-utils-pytorch-inputshapesettereeg">
+<h1>moabb.pipelines.utils_pytorch.InputShapeSetterEEG<a class="headerlink" href="#moabb-pipelines-utils-pytorch-inputshapesettereeg" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.pipelines.utils_pytorch.InputShapeSetterEEG">
+<span class="sig-prename descclassname"><span class="pre">moabb.pipelines.utils_pytorch.</span></span><span class="sig-name descname"><span class="pre">InputShapeSetterEEG</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params_list=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_dim_fn=&lt;function</span> <span class="pre">get_shape_from_baseconcat&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_name='module'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/pipelines/utils_pytorch.py#L97-L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.pipelines.utils_pytorch.InputShapeSetterEEG" title="Permalink to this definition">#</a></dt>
+<dd><p>Sets the input dimension of the PyTorch module to the input dimension
+of the training data.
+This can be of use when the shape of X is not known beforehand,
+e.g. when using a skorch model within an sklearn pipeline and
+grid-searching feature transformers, or using feature selection
+methods.InputShapeSetterEEG
+Basic usage:</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>params_list</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – The list of parameters that define the
+input dimension in its <code class="docutils literal notranslate"><span class="pre">__init__</span></code> method.
+Usually the mandatory parameters from the model.</p></li>
+<li><p><strong>input_dim_fn</strong> (<em>callable</em><em>, </em><em>None</em><em> (</em><em>default=None</em><em>)</em>) – In case your <code class="docutils literal notranslate"><span class="pre">X</span></code> value is more complex and deriving the input
+dimension is not as easy as <code class="docutils literal notranslate"><span class="pre">X.shape[-1]</span></code> you can pass a callable
+to this parameter which takes <code class="docutils literal notranslate"><span class="pre">X</span></code> and returns the input dimension.</p></li>
+<li><p><strong>module_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>default='module'</em><em>)</em>) – Only needs change when you are using more than one module in your
+skorch model (e.g., in case of GANs).</p></li>
+</ul>
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+<h1>moabb.set_download_dir<a class="headerlink" href="#moabb-set-download-dir" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.set_download_dir">
+<span class="sig-prename descclassname"><span class="pre">moabb.</span></span><span class="sig-name descname"><span class="pre">set_download_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/utils.py#L134-L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.set_download_dir" title="Permalink to this definition">#</a></dt>
+<dd><p>Set the download directory if required to change from default mne path.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – </p></li>
+<li><p><strong>location</strong> (<em>The new storage</em>) – </p></li>
+<li><p><strong>exist</strong> (<em>and MNE_DATA config does not</em>) – </p></li>
+<li><p><strong>the</strong> (<em>a warning is raised and</em>) – </p></li>
+<li><p><strong>created</strong> (<em>path is</em>) – </p></li>
+<li><p><strong>None</strong> (<em>If</em>) – </p></li>
+<li><p><strong>exist</strong> – </p></li>
+<li><p><strong>the</strong> – </p></li>
+<li><p><strong>directory</strong> (<em>storage location is set to the MNE default</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-set-download-dir">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.set_download_dir</span></code><a class="headerlink" href="#examples-using-moabb-set-download-dir" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This is a minimal example to demonstrate how to change the default data download directory to a..."><img alt="Change Download Directory" src="../_images/sphx_glr_changing_download_directory_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/changing_download_directory.html#sphx-glr-auto-examples-changing-download-directory-py"><span class="std std-ref">Change Download Directory</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Change Download Directory</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-set-download-dir">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.set_download_dir</span></code></a></li>
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+  <section id="moabb-set-log-level">
+<h1>moabb.set_log_level<a class="headerlink" href="#moabb-set-log-level" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.set_log_level">
+<span class="sig-prename descclassname"><span class="pre">moabb.</span></span><span class="sig-name descname"><span class="pre">set_log_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'INFO'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/utils.py#L117-L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.set_log_level" title="Permalink to this definition">#</a></dt>
+<dd><p>Set log level.</p>
+<p>Set the general log level. Use one of the levels supported by python
+logging, i.e.: DEBUG, INFO, WARNING, ERROR, CRITICAL</p>
+</dd></dl>
+
+<section id="examples-using-moabb-set-log-level">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.set_log_level</span></code><a class="headerlink" href="#examples-using-moabb-set-log-level" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to track the CO2 footprint using `CodeCarbon library &lt;https..."><img alt="Benchmarking with MOABB showing the CO2 footprint" src="../_images/sphx_glr_example_codecarbon_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/example_codecarbon.html#sphx-glr-auto-examples-example-codecarbon-py"><span class="std std-ref">Benchmarking with MOABB showing the CO2 footprint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB showing the CO2 footprint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use load the pretrained pipeline in MOABB."><img alt="Load Model (Scikit, Pytorch, Keras) with MOABB" src="../_images/sphx_glr_load_model_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/load_model.html#sphx-glr-auto-examples-load-model-py"><span class="std std-ref">Load Model (Scikit, Pytorch, Keras) with MOABB</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load Model (Scikit, Pytorch, Keras) with MOABB</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The Brain Imaging Data Structure (BIDS) format is standard for storing neuroimaging data. It fo..."><img alt="Convert a MOABB dataset to BIDS" src="../_images/sphx_glr_noplot_bids_conversion_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/noplot_bids_conversion.html#sphx-glr-auto-examples-noplot-bids-conversion-py"><span class="std std-ref">Convert a MOABB dataset to BIDS</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convert a MOABB dataset to BIDS</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Examples of how to use MOABB to benchmark pipelines." src="../_images/sphx_glr_plot_benchmark_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark.html#sphx-glr-auto-examples-plot-benchmark-py"><span class="std std-ref">Examples of how to use MOABB to benchmark pipelines.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of how to use MOABB to benchmark pipelines.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use MOABB to benchmark a set of pipelines on all available datasets. ..."><img alt="Benchmarking with MOABB with Grid Search" src="../_images/sphx_glr_plot_benchmark_grid_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_grid_search.html#sphx-glr-auto-examples-plot-benchmark-grid-search-py"><span class="std std-ref">Benchmarking with MOABB with Grid Search</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking with MOABB with Grid Search</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show how to perform a cross session motor imagery analysis on the very popular dat..."><img alt="Cross-Session Motor Imagery" src="../_images/sphx_glr_plot_cross_session_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_motor_imagery.html#sphx-glr-auto-examples-plot-cross-session-motor-imagery-py"><span class="std std-ref">Cross-Session Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a cross-session analysis on two MI datasets using a CSP+LDA p..."><img alt="Cross-Session on Multiple Datasets" src="../_images/sphx_glr_plot_cross_session_multiple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_session_multiple_datasets.html#sphx-glr-auto-examples-plot-cross-session-multiple-datasets-py"><span class="std std-ref">Cross-Session on Multiple Datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Session on Multiple Datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="- Riemannian Geometry - CCA - TRCA - MsetCCA"><img alt="Cross-Subject SSVEP" src="../_images/sphx_glr_plot_cross_subject_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_cross_subject_ssvep.html#sphx-glr-auto-examples-plot-cross-subject-ssvep-py"><span class="std std-ref">Cross-Subject SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-Subject SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how intermediate data processing states can be cached on disk to speed up th..."><img alt="Cache on disk intermediate data processing states" src="../_images/sphx_glr_plot_disk_cache_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_disk_cache.html#sphx-glr-auto-examples-plot-disk-cache-py"><span class="std std-ref">Cache on disk intermediate data processing states</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cache on disk intermediate data processing states</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to process a dataset using the moabb.paradigms.FixedIntervalWindowsProce..."><img alt="Fixed interval windows processing" src="../_images/sphx_glr_plot_fixed_interval_windows_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_fixed_interval_windows.html#sphx-glr-auto-examples-plot-fixed-interval-windows-py"><span class="std std-ref">Fixed interval windows processing</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fixed interval windows processing</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis on three different P300 datasets."><img alt="Within Session P300" src="../_images/sphx_glr_plot_within_session_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_p300.html#sphx-glr-auto-examples-plot-within-session-p300-py"><span class="std std-ref">Within Session P300</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This Example show how to perform a within-session SSVEP analysis on the MAMEM dataset 3, using ..."><img alt="Within Session SSVEP" src="../_images/sphx_glr_plot_within_session_ssvep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_within_session_ssvep.html#sphx-glr-auto-examples-plot-within-session-ssvep-py"><span class="std std-ref">Within Session SSVEP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session SSVEP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example show a comparison of CSP versus FilterBank CSP on the very popular dataset 2a from..."><img alt="FilterBank CSP versus CSP" src="../_images/sphx_glr_plot_filterbank_csp_vs_csp_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html#sphx-glr-auto-examples-advanced-examples-plot-filterbank-csp-vs-csp-py"><span class="std std-ref">FilterBank CSP versus CSP</span></a></p>
+  <div class="sphx-glr-thumbnail-title">FilterBank CSP versus CSP</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use machine learning pipeline based on MNE Epochs instead of Numpy ar..."><img alt="MNE Epochs-based pipelines" src="../_images/sphx_glr_plot_mne_and_scikit_estimators_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_mne_and_scikit_estimators.html#sphx-glr-auto-examples-advanced-examples-plot-mne-and-scikit-estimators-py"><span class="std std-ref">MNE Epochs-based pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Epochs-based pipelines</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The MOABB codebase comes with convenience plotting utilities and some statistical testing. This..."><img alt="Statistical Analysis" src="../_images/sphx_glr_plot_statistical_analysis_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_examples/plot_statistical_analysis.html#sphx-glr-auto-examples-advanced-examples-plot-statistical-analysis-py"><span class="std std-ref">Statistical Analysis</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Statistical Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_external_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/external/plot_learning_curve_p300_external.html#sphx-glr-auto-examples-external-plot-learning-curve-p300-external-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session motor imagery analysis on the very popular d..."><img alt="Within Session Motor Imagery with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_motor_imagery.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-motor-imagery-py"><span class="std std-ref">Within Session Motor Imagery with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session Motor Imagery with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform a within session analysis while also creating learning curves..."><img alt="Within Session P300 with Learning Curve" src="../_images/sphx_glr_plot_learning_curve_p300_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/learning_curve/plot_learning_curve_p300.html#sphx-glr-auto-examples-learning-curve-plot-learning-curve-p300-py"><span class="std std-ref">Within Session P300 with Learning Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Within Session P300 with Learning Curve</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial takes you through a basic working example of how to use this codebase, including ..."><img alt="Tutorial 0: Getting Started" src="../_images/sphx_glr_plot_Getting_Started_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/plot_Getting_Started.html#sphx-glr-auto-tutorials-plot-getting-started-py"><span class="std std-ref">Tutorial 0: Getting Started</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 0: Getting Started</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will go through all the steps to make a simple BCI classification task, dow..."><img alt="Tutorial 1: Simple Motor Imagery" src="../_images/sphx_glr_tutorial_1_simple_example_motor_imagery_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_1_simple_example_motor_imagery.html#sphx-glr-auto-tutorials-tutorial-1-simple-example-motor-imagery-py"><span class="std std-ref">Tutorial 1: Simple Motor Imagery</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 1: Simple Motor Imagery</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We extend the previous example to a case where we want to analyze the score of a classifier wit..."><img alt="Tutorial 2: Using multiple datasets" src="../_images/sphx_glr_tutorial_2_using_mulitple_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_2_using_mulitple_datasets.html#sphx-glr-auto-tutorials-tutorial-2-using-mulitple-datasets-py"><span class="std std-ref">Tutorial 2: Using multiple datasets</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 2: Using multiple datasets</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this last part, we extend the previous example by assessing the classification score of not ..."><img alt="Tutorial 3: Benchmarking multiple pipelines" src="../_images/sphx_glr_tutorial_3_benchmarking_multiple_pipelines_thumb.png" />
+<p><a class="reference internal" href="../auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html#sphx-glr-auto-tutorials-tutorial-3-benchmarking-multiple-pipelines-py"><span class="std std-ref">Tutorial 3: Benchmarking multiple pipelines</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tutorial 3: Benchmarking multiple pipelines</div>
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+  <section id="moabb-setup-seed">
+<h1>moabb.setup_seed<a class="headerlink" href="#moabb-setup-seed" title="Permalink to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="moabb.setup_seed">
+<span class="sig-prename descclassname"><span class="pre">moabb.</span></span><span class="sig-name descname"><span class="pre">setup_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/develop/moabb/utils.py#L95-L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moabb.setup_seed" title="Permalink to this definition">#</a></dt>
+<dd><p>Set the seed for random, numpy, TensorFlow and PyTorch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The random seed to use.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-moabb-setup-seed">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">moabb.setup_seed</span></code><a class="headerlink" href="#examples-using-moabb-setup-seed" title="Permalink to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use load the pretrained pipeline in MOABB."><img alt="Load Model (Scikit, Pytorch, Keras) with MOABB" src="../_images/sphx_glr_load_model_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/load_model.html#sphx-glr-auto-examples-load-model-py"><span class="std std-ref">Load Model (Scikit, Pytorch, Keras) with MOABB</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load Model (Scikit, Pytorch, Keras) with MOABB</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Tensorflow deep net architectures"><img alt="Benchmarking on MOABB with Tensorflow deep net architectures" src="../_images/sphx_glr_plot_benchmark_DL_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_DL.html#sphx-glr-auto-examples-plot-benchmark-dl-py"><span class="std std-ref">Benchmarking on MOABB with Tensorflow deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Tensorflow deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures"><img alt="Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures" src="../_images/sphx_glr_plot_benchmark_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_benchmark_braindecode.html#sphx-glr-auto-examples-plot-benchmark-braindecode-py"><span class="std std-ref">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cross-session motor imagery with deep learning EEGNet v4 model"><img alt="Cross-session motor imagery with deep learning EEGNet v4 model" src="../_images/sphx_glr_plot_braindecode_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/plot_braindecode.html#sphx-glr-auto-examples-plot-braindecode-py"><span class="std std-ref">Cross-session motor imagery with deep learning EEGNet v4 model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cross-session motor imagery with deep learning EEGNet v4 model</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-moabb-setup-seed">Examples using <code class="docutils literal notranslate"><span class="pre">moabb.setup_seed</span></code></a></li>
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\ No newline at end of file
diff --git a/docs/genindex.html b/docs/genindex.html
new file mode 100644
index 00000000..ca65f650
--- /dev/null
+++ b/docs/genindex.html
@@ -0,0 +1,1271 @@
+
+
+<!DOCTYPE html>
+
+
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+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Index &#8212; moabb 1.0.0-dev documentation</title>
+  
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+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
+
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+    <script>DOCUMENTATION_OPTIONS.pagename = 'genindex';</script>
+    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
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+    <link rel="search" title="Search" href="search.html" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
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+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
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+         placeholder="Search the docs ..."
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+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+  </div>
+
+
+<script>
+document.write(`<div class="bd-header-announcement"></div>`);
+fetch("https://raw.githubusercontent.com/NeuroTechX/moabb/develop/docs/source/_templates/custom-template.html")
+  .then(res => {return res.text();})
+  .then(data => {
+    div = document.querySelector(".bd-header-announcement");
+    div.classList.add(...["bd-header-announcement", "container-fluid"]);
+    div.innerHTML = `<div class="bd-header-announcement__content">${data}</div>`;
+  })
+  .catch(error => {
+    console.log("[PST]: Failed to load announcement at: https://raw.githubusercontent.com/NeuroTechX/moabb/develop/docs/source/_templates/custom-template.html");
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+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
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+  
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+    
+    <img src="_static/moabb_logo.svg" class="logo__image only-light" alt="Logo image"/>
+    <script>document.write(`<img src="_static/moabb_logo.svg" class="logo__image only-dark" alt="Logo image"/>`);</script>
+  
+  
+</a></div>
+    
+  </div>
+  
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+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
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+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="overview.html">
+                        Overview
+                      </a>
+                    </li>
+                
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_tutorials/index.html">
+                        Getting Started
+                      </a>
+                    </li>
+                
+            <div class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
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+                    
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+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Gallery
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
+                        API reference
+                      </a>
+                    </li>
+                
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+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        Release notes
+                      </a>
+                    </li>
+                
+                </div>
+            </div>
+            
+  </ul>
+</nav></div>
+      
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+    
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+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+  </button>
+`);
+</script>
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+      
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+</script></div>
+      
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+  
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+    <div class="navbar-persistent--mobile">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
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+`);
+</script>
+    </div>
+  
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+
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+  
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      <div class="bd-sidebar-primary bd-sidebar hide-on-wide">
+        
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+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="overview.html">
+                        Overview
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_tutorials/index.html">
+                        Getting Started
+                      </a>
+                    </li>
+                
+            <div class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
+                    More
+                </button>
+                <div class="dropdown-menu">
+                    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Gallery
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
+                        API reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        Release notes
+                      </a>
+                    </li>
+                
+                </div>
+            </div>
+            
+  </ul>
+</nav></div>
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+        
+      </div>
+    
+  </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article"></div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#Z"><strong>Z</strong></a>
+ 
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig.__hash__">__hash__ (moabb.datasets.base.CacheConfig attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI">AlexMI (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.features.AugmentedDataset.html#moabb.pipelines.features.AugmentedDataset">AugmentedDataset (class in moabb.pipelines.features)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset">BaseDataset (class in moabb.datasets.base)</a>
+</li>
+      <li><a href="generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation">BaseEvaluation (class in moabb.evaluations.base)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html#moabb.paradigms.BaseFixedIntervalWindowsProcessing">BaseFixedIntervalWindowsProcessing (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery">BaseMotorImagery (class in moabb.paradigms.motor_imagery)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.p300.BaseP300.html#moabb.paradigms.p300.BaseP300">BaseP300 (class in moabb.paradigms.p300)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.base.BaseParadigm.html#moabb.paradigms.base.BaseParadigm">BaseParadigm (class in moabb.paradigms.base)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing">BaseProcessing (class in moabb.paradigms.base)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP">BaseSSVEP (class in moabb.paradigms.ssvep)</a>
+</li>
+      <li><a href="generated/moabb.benchmark.html#moabb.benchmark">benchmark() (in module moabb)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BI2012.html#moabb.datasets.BI2012">BI2012 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BI2013a.html#moabb.datasets.BI2013a">BI2013a (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a">BI2014a (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.compound_dataset.BI2014a_Il.html#moabb.datasets.compound_dataset.BI2014a_Il">BI2014a_Il (class in moabb.datasets.compound_dataset)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BI2014b.html#moabb.datasets.BI2014b">BI2014b (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.compound_dataset.BI2014b_Il.html#moabb.datasets.compound_dataset.BI2014b_Il">BI2014b_Il (class in moabb.datasets.compound_dataset)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BI2015a.html#moabb.datasets.BI2015a">BI2015a (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.compound_dataset.BI2015a_Il.html#moabb.datasets.compound_dataset.BI2015a_Il">BI2015a_Il (class in moabb.datasets.compound_dataset)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BI2015b.html#moabb.datasets.BI2015b">BI2015b (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.compound_dataset.BI2015b_Il.html#moabb.datasets.compound_dataset.BI2015b_Il">BI2015b_Il (class in moabb.datasets.compound_dataset)</a>
+</li>
+      <li><a href="generated/moabb.datasets.compound_dataset.BI_Il.html#moabb.datasets.compound_dataset.BI_Il">BI_Il (class in moabb.datasets.compound_dataset)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001">BNCI2014_001 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2014_002.html#moabb.datasets.BNCI2014_002">BNCI2014_002 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004">BNCI2014_004 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2014_008.html#moabb.datasets.BNCI2014_008">BNCI2014_008 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009">BNCI2014_009 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2015_001.html#moabb.datasets.BNCI2015_001">BNCI2015_001 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2015_003.html#moabb.datasets.BNCI2015_003">BNCI2015_003 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.BNCI2015_004.html#moabb.datasets.BNCI2015_004">BNCI2015_004 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html#moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader">BraindecodeDatasetLoader() (in module moabb.pipelines.utils_pytorch)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig">CacheConfig (class in moabb.datasets.base)</a>
+</li>
+      <li><a href="generated/moabb.datasets.CastillosBurstVEP100.html#moabb.datasets.CastillosBurstVEP100">CastillosBurstVEP100 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.CastillosBurstVEP40.html#moabb.datasets.CastillosBurstVEP40">CastillosBurstVEP40 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.CastillosCVEP100.html#moabb.datasets.CastillosCVEP100">CastillosCVEP100 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.CastillosCVEP40.html#moabb.datasets.CastillosCVEP40">CastillosCVEP40 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD">Cattan2019_PHMD (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR">Cattan2019_VR (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.compound_dataset.Cattan2019_VR_Il.html#moabb.datasets.compound_dataset.Cattan2019_VR_Il">Cattan2019_VR_Il (class in moabb.datasets.compound_dataset)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017">Cho2017 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.classes_">classes_ (moabb.pipelines.classification.SSVEP_TRCA attribute)</a>
+</li>
+      <li><a href="generated/moabb.analysis.meta_analysis.collapse_session_scores.html#moabb.analysis.meta_analysis.collapse_session_scores">collapse_session_scores() (in module moabb.analysis.meta_analysis)</a>
+</li>
+      <li><a href="generated/moabb.analysis.meta_analysis.combine_effects.html#moabb.analysis.meta_analysis.combine_effects">combine_effects() (in module moabb.analysis.meta_analysis)</a>
+</li>
+      <li><a href="generated/moabb.analysis.meta_analysis.combine_pvalues.html#moabb.analysis.meta_analysis.combine_pvalues">combine_pvalues() (in module moabb.analysis.meta_analysis)</a>
+</li>
+      <li><a href="generated/moabb.analysis.meta_analysis.compute_dataset_statistics.html#moabb.analysis.meta_analysis.compute_dataset_statistics">compute_dataset_statistics() (in module moabb.analysis.meta_analysis)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.utils.create_pipeline_from_config.html#moabb.pipelines.utils.create_pipeline_from_config">create_pipeline_from_config() (in module moabb.pipelines.utils)</a>
+</li>
+      <li><a href="generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation">CrossSessionEvaluation (class in moabb.evaluations)</a>
+</li>
+      <li><a href="generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation">CrossSubjectEvaluation (class in moabb.evaluations)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.CVEP.html#moabb.paradigms.CVEP">CVEP (class in moabb.paradigms)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.download.data_dl.html#moabb.datasets.download.data_dl">data_dl() (in module moabb.datasets.download)</a>
+</li>
+      <li><a href="generated/moabb.datasets.download.data_path.html#moabb.datasets.download.data_path">data_path() (in module moabb.datasets.download)</a>
+
+      <ul>
+        <li><a href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI.data_path">(moabb.datasets.AlexMI method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset.data_path">(moabb.datasets.base.BaseDataset method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.BI2012.html#moabb.datasets.BI2012.data_path">(moabb.datasets.BI2012 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.BI2013a.html#moabb.datasets.BI2013a.data_path">(moabb.datasets.BI2013a method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a.data_path">(moabb.datasets.BI2014a method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.BI2014b.html#moabb.datasets.BI2014b.data_path">(moabb.datasets.BI2014b method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.BI2015a.html#moabb.datasets.BI2015a.data_path">(moabb.datasets.BI2015a method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.BI2015b.html#moabb.datasets.BI2015b.data_path">(moabb.datasets.BI2015b method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Cattan2019_PHMD.html#moabb.datasets.Cattan2019_PHMD.data_path">(moabb.datasets.Cattan2019_PHMD method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR.data_path">(moabb.datasets.Cattan2019_VR method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017.data_path">(moabb.datasets.Cho2017 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.DemonsP300.html#moabb.datasets.DemonsP300.data_path">(moabb.datasets.DemonsP300 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.EPFLP300.html#moabb.datasets.EPFLP300.data_path">(moabb.datasets.EPFLP300 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.fake.FakeDataset.html#moabb.datasets.fake.FakeDataset.data_path">(moabb.datasets.fake.FakeDataset method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.GrosseWentrup2009.html#moabb.datasets.GrosseWentrup2009.data_path">(moabb.datasets.GrosseWentrup2009 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016.data_path">(moabb.datasets.Kalunga2016 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Nakanishi2015.html#moabb.datasets.Nakanishi2015.data_path">(moabb.datasets.Nakanishi2015 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Ofner2017.html#moabb.datasets.Ofner2017.data_path">(moabb.datasets.Ofner2017 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI.data_path">(moabb.datasets.PhysionetMI method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017.data_path">(moabb.datasets.Schirrmeister2017 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019.data_path">(moabb.datasets.Sosulski2019 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Thielen2015.html#moabb.datasets.Thielen2015.data_path">(moabb.datasets.Thielen2015 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Thielen2021.html#moabb.datasets.Thielen2021.data_path">(moabb.datasets.Thielen2021 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Wang2016.html#moabb.datasets.Wang2016.data_path">(moabb.datasets.Wang2016 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014.data_path">(moabb.datasets.Weibo2014 method)</a>
+</li>
+        <li><a href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016.data_path">(moabb.datasets.Zhou2016 method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.utils.dataset_search.html#moabb.datasets.utils.dataset_search">dataset_search() (in module moabb.datasets.utils)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.datasets">datasets (moabb.paradigms.base.BaseProcessing property)</a>
+
+      <ul>
+        <li><a href="generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html#moabb.paradigms.BaseFixedIntervalWindowsProcessing.datasets">(moabb.paradigms.BaseFixedIntervalWindowsProcessing property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery.datasets">(moabb.paradigms.FilterBankMotorImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.datasets">(moabb.paradigms.motor_imagery.BaseMotorImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery.datasets">(moabb.paradigms.MotorImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.p300.BaseP300.html#moabb.paradigms.p300.BaseP300.datasets">(moabb.paradigms.p300.BaseP300 property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.datasets">(moabb.paradigms.ssvep.BaseSSVEP property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.datasets.DemonsP300.html#moabb.datasets.DemonsP300">DemonsP300 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset.download">download() (moabb.datasets.base.BaseDataset method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.utils_deep_model.EEGNet.html#moabb.pipelines.utils_deep_model.EEGNet">EEGNet() (in module moabb.pipelines.utils_deep_model)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html#moabb.pipelines.utils_deep_model.EEGNet_TC">EEGNet_TC() (in module moabb.pipelines.utils_deep_model)</a>
+</li>
+      <li><a href="generated/moabb.datasets.EPFLP300.html#moabb.datasets.EPFLP300">EPFLP300 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.evaluate">evaluate() (moabb.evaluations.base.BaseEvaluation method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation.evaluate">(moabb.evaluations.CrossSessionEvaluation method)</a>
+</li>
+        <li><a href="generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation.evaluate">(moabb.evaluations.CrossSubjectEvaluation method)</a>
+</li>
+        <li><a href="generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation.evaluate">(moabb.evaluations.WithinSessionEvaluation method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal">ExtendedSSVEPSignal (class in moabb.pipelines.features)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.fake.FakeDataset.html#moabb.datasets.fake.FakeDataset">FakeDataset (class in moabb.datasets.fake)</a>
+</li>
+      <li><a href="generated/moabb.datasets.fake.FakeVirtualRealityDataset.html#moabb.datasets.fake.FakeVirtualRealityDataset">FakeVirtualRealityDataset (class in moabb.datasets.fake)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.fb_coefs">fb_coefs (moabb.pipelines.classification.SSVEP_TRCA attribute)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.motor_imagery.FilterBank.html#moabb.paradigms.motor_imagery.FilterBank">FilterBank (class in moabb.paradigms.motor_imagery)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.utils.FilterBank.html#moabb.pipelines.utils.FilterBank">FilterBank() (in module moabb.pipelines.utils)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.FilterBankCVEP.html#moabb.paradigms.FilterBankCVEP">FilterBankCVEP (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html#moabb.paradigms.FilterBankFixedIntervalWindowsProcessing">FilterBankFixedIntervalWindowsProcessing (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery">FilterBankLeftRightImagery (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery">FilterBankMotorImagery (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP">FilterBankSSVEP (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.datasets.utils.find_intersecting_channels.html#moabb.datasets.utils.find_intersecting_channels">find_intersecting_channels() (in module moabb.datasets.utils)</a>
+</li>
+      <li><a href="generated/moabb.analysis.meta_analysis.find_significant_differences.html#moabb.analysis.meta_analysis.find_significant_differences">find_significant_differences() (in module moabb.analysis.meta_analysis)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA.fit">fit() (moabb.pipelines.classification.SSVEP_CCA method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA.fit">(moabb.pipelines.classification.SSVEP_MsetCCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.fit">(moabb.pipelines.classification.SSVEP_TRCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.csp.TRCSP.html#moabb.pipelines.csp.TRCSP.fit">(moabb.pipelines.csp.TRCSP method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal.fit">(moabb.pipelines.features.ExtendedSSVEPSignal method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.features.FM.html#moabb.pipelines.features.FM.fit">(moabb.pipelines.features.FM method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance.fit">(moabb.pipelines.features.LogVariance method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing">FixedIntervalWindowsProcessing (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.features.FM.html#moabb.pipelines.features.FM">FM (class in moabb.pipelines.features)</a>
+</li>
+      <li><a href="generated/moabb.datasets.download.fs_get_file_hash.html#moabb.datasets.download.fs_get_file_hash">fs_get_file_hash() (in module moabb.datasets.download)</a>
+</li>
+      <li><a href="generated/moabb.datasets.download.fs_get_file_id.html#moabb.datasets.download.fs_get_file_id">fs_get_file_id() (in module moabb.datasets.download)</a>
+</li>
+      <li><a href="generated/moabb.datasets.download.fs_get_file_list.html#moabb.datasets.download.fs_get_file_list">fs_get_file_list() (in module moabb.datasets.download)</a>
+</li>
+      <li><a href="generated/moabb.datasets.download.fs_get_file_name.html#moabb.datasets.download.fs_get_file_name">fs_get_file_name() (in module moabb.datasets.download)</a>
+</li>
+      <li><a href="generated/moabb.datasets.download.fs_issue_request.html#moabb.datasets.download.fs_issue_request">fs_issue_request() (in module moabb.datasets.download)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR.get_block_repetition">get_block_repetition() (moabb.datasets.Cattan2019_VR method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.datasets.fake.FakeVirtualRealityDataset.html#moabb.datasets.fake.FakeVirtualRealityDataset.get_block_repetition">(moabb.datasets.fake.FakeVirtualRealityDataset method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.base.BaseDataset.html#moabb.datasets.base.BaseDataset.get_data">get_data() (moabb.datasets.base.BaseDataset method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data">(moabb.paradigms.base.BaseProcessing method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.datasets.GrosseWentrup2009.html#moabb.datasets.GrosseWentrup2009">GrosseWentrup2009 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Huebner2017.html#moabb.datasets.Huebner2017">Huebner2017 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Huebner2018.html#moabb.datasets.Huebner2018">Huebner2018 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html#moabb.pipelines.utils_pytorch.InputShapeSetterEEG">InputShapeSetterEEG() (in module moabb.pipelines.utils_pytorch)</a>
+</li>
+      <li><a href="generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.is_valid">is_valid() (moabb.evaluations.base.BaseEvaluation method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation.is_valid">(moabb.evaluations.CrossSessionEvaluation method)</a>
+</li>
+        <li><a href="generated/moabb.evaluations.CrossSubjectEvaluation.html#moabb.evaluations.CrossSubjectEvaluation.is_valid">(moabb.evaluations.CrossSubjectEvaluation method)</a>
+</li>
+        <li><a href="generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation.is_valid">(moabb.evaluations.WithinSessionEvaluation method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.is_valid">(moabb.paradigms.base.BaseProcessing method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html#moabb.paradigms.BaseFixedIntervalWindowsProcessing.is_valid">(moabb.paradigms.BaseFixedIntervalWindowsProcessing method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery.is_valid">(moabb.paradigms.FilterBankMotorImagery method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.is_valid">(moabb.paradigms.motor_imagery.BaseMotorImagery method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery.is_valid">(moabb.paradigms.MotorImagery method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.p300.BaseP300.html#moabb.paradigms.p300.BaseP300.is_valid">(moabb.paradigms.p300.BaseP300 method)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.is_valid">(moabb.paradigms.ssvep.BaseSSVEP method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016">Kalunga2016 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#moabb.pipelines.deep_learning.KerasDeepConvNet">KerasDeepConvNet (class in moabb.pipelines.deep_learning)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#moabb.pipelines.deep_learning.KerasEEGITNet">KerasEEGITNet (class in moabb.pipelines.deep_learning)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#moabb.pipelines.deep_learning.KerasEEGNet_8_2">KerasEEGNet_8_2 (class in moabb.pipelines.deep_learning)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#moabb.pipelines.deep_learning.KerasEEGNeX">KerasEEGNeX (class in moabb.pipelines.deep_learning)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#moabb.pipelines.deep_learning.KerasEEGTCNet">KerasEEGTCNet (class in moabb.pipelines.deep_learning)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#moabb.pipelines.deep_learning.KerasShallowConvNet">KerasShallowConvNet (class in moabb.pipelines.deep_learning)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Lee2019_ERP.html#moabb.datasets.Lee2019_ERP">Lee2019_ERP (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Lee2019_MI.html#moabb.datasets.Lee2019_MI">Lee2019_MI (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Lee2019_SSVEP.html#moabb.datasets.Lee2019_SSVEP">Lee2019_SSVEP (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery">LeftRightImagery (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance">LogVariance (class in moabb.pipelines.features)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.base.CacheConfig.html#moabb.datasets.base.CacheConfig.make">make() (moabb.datasets.base.CacheConfig class method)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.make_labels_pipeline">make_labels_pipeline() (moabb.paradigms.base.BaseProcessing method)</a>
+</li>
+      <li><a href="generated/moabb.make_process_pipelines.html#moabb.make_process_pipelines">make_process_pipelines() (in module moabb)</a>
+
+      <ul>
+        <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.make_process_pipelines">(moabb.paradigms.base.BaseProcessing method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.datasets.MAMEM1.html#moabb.datasets.MAMEM1">MAMEM1 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.MAMEM2.html#moabb.datasets.MAMEM2">MAMEM2 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3">MAMEM3 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.match_all">match_all() (moabb.paradigms.base.BaseProcessing method)</a>
+</li>
+      <li><a href="generated/moabb.analysis.plotting.meta_analysis_plot.html#moabb.analysis.plotting.meta_analysis_plot">meta_analysis_plot() (in module moabb.analysis.plotting)</a>
+</li>
+      <li>
+    moabb
+
+      <ul>
+        <li><a href="api.html#module-moabb">module</a>, <a href="utils.html#module-moabb">[1]</a>
+</li>
+      </ul></li>
+      <li>
+    moabb.analysis
+
+      <ul>
+        <li><a href="analysis.html#module-moabb.analysis">module</a>, <a href="api.html#module-moabb.analysis">[1]</a>
+</li>
+      </ul></li>
+      <li>
+    moabb.datasets
+
+      <ul>
+        <li><a href="api.html#module-moabb.datasets">module</a>, <a href="dataset_summary.html#module-moabb.datasets">[1]</a>, <a href="datasets.html#module-moabb.datasets">[2]</a>
+</li>
+      </ul></li>
+      <li>
+    moabb.datasets.compound_dataset
+
+      <ul>
+        <li><a href="api.html#module-moabb.datasets.compound_dataset">module</a>, <a href="dataset_summary.html#module-moabb.datasets.compound_dataset">[1]</a>, <a href="datasets.html#module-moabb.datasets.compound_dataset">[2]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    moabb.evaluations
+
+      <ul>
+        <li><a href="api.html#module-moabb.evaluations">module</a>, <a href="evaluations.html#module-moabb.evaluations">[1]</a>
+</li>
+      </ul></li>
+      <li>
+    moabb.paradigms
+
+      <ul>
+        <li><a href="api.html#module-moabb.paradigms">module</a>, <a href="paradigms.html#module-moabb.paradigms">[1]</a>
+</li>
+      </ul></li>
+      <li>
+    moabb.pipelines
+
+      <ul>
+        <li><a href="api.html#module-moabb.pipelines">module</a>, <a href="pipelines.html#module-moabb.pipelines">[1]</a>
+</li>
+      </ul></li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="api.html#module-moabb">moabb</a>, <a href="utils.html#module-moabb">[1]</a>
+</li>
+        <li><a href="analysis.html#module-moabb.analysis">moabb.analysis</a>, <a href="api.html#module-moabb.analysis">[1]</a>
+</li>
+        <li><a href="api.html#module-moabb.datasets">moabb.datasets</a>, <a href="dataset_summary.html#module-moabb.datasets">[1]</a>, <a href="datasets.html#module-moabb.datasets">[2]</a>
+</li>
+        <li><a href="api.html#module-moabb.datasets.compound_dataset">moabb.datasets.compound_dataset</a>, <a href="dataset_summary.html#module-moabb.datasets.compound_dataset">[1]</a>, <a href="datasets.html#module-moabb.datasets.compound_dataset">[2]</a>
+</li>
+        <li><a href="api.html#module-moabb.evaluations">moabb.evaluations</a>, <a href="evaluations.html#module-moabb.evaluations">[1]</a>
+</li>
+        <li><a href="api.html#module-moabb.paradigms">moabb.paradigms</a>, <a href="paradigms.html#module-moabb.paradigms">[1]</a>
+</li>
+        <li><a href="api.html#module-moabb.pipelines">moabb.pipelines</a>, <a href="pipelines.html#module-moabb.pipelines">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery">MotorImagery (class in moabb.paradigms)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.n_classes">n_classes (moabb.pipelines.classification.SSVEP_TRCA attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Nakanishi2015.html#moabb.datasets.Nakanishi2015">Nakanishi2015 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Ofner2017.html#moabb.datasets.Ofner2017">Ofner2017 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.paradigms.P300.html#moabb.paradigms.P300">P300 (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.analysis.plotting.paired_plot.html#moabb.analysis.plotting.paired_plot">paired_plot() (in module moabb.analysis.plotting)</a>
+</li>
+      <li><a href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI">PhysionetMI (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA.predict">predict() (moabb.pipelines.classification.SSVEP_CCA method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA.predict">(moabb.pipelines.classification.SSVEP_MsetCCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.predict">(moabb.pipelines.classification.SSVEP_TRCA method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA.predict_proba">predict_proba() (moabb.pipelines.classification.SSVEP_CCA method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA.predict_proba">(moabb.pipelines.classification.SSVEP_MsetCCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.predict_proba">(moabb.pipelines.classification.SSVEP_TRCA method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.prepare_process">prepare_process() (moabb.paradigms.base.BaseProcessing method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.prepare_process">(moabb.paradigms.ssvep.BaseSSVEP method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.evaluations.base.BaseEvaluation.html#moabb.evaluations.base.BaseEvaluation.process">process() (moabb.evaluations.base.BaseEvaluation method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.DemonsP300.html#moabb.datasets.DemonsP300.read_hdf">read_hdf() (moabb.datasets.DemonsP300 class method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.Reference">Reference (moabb.pipelines.classification.SSVEP_TRCA attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017">Schirrmeister2017 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.analysis.plotting.score_plot.html#moabb.analysis.plotting.score_plot">score_plot() (in module moabb.analysis.plotting)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.base.BaseParadigm.html#moabb.paradigms.base.BaseParadigm.scoring">scoring (moabb.paradigms.base.BaseParadigm property)</a>
+
+      <ul>
+        <li><a href="generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery.scoring">(moabb.paradigms.FilterBankLeftRightImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery.scoring">(moabb.paradigms.FilterBankMotorImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery.scoring">(moabb.paradigms.LeftRightImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery.scoring">(moabb.paradigms.motor_imagery.BaseMotorImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery.scoring">(moabb.paradigms.MotorImagery property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.P300.html#moabb.paradigms.P300.scoring">(moabb.paradigms.P300 property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.p300.BaseP300.html#moabb.paradigms.p300.BaseP300.scoring">(moabb.paradigms.p300.BaseP300 property)</a>
+</li>
+        <li><a href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.scoring">(moabb.paradigms.ssvep.BaseSSVEP property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.set_download_dir.html#moabb.set_download_dir">set_download_dir() (in module moabb)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA.set_fit_request">set_fit_request() (moabb.pipelines.classification.SSVEP_CCA method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA.set_fit_request">(moabb.pipelines.classification.SSVEP_MsetCCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#moabb.pipelines.deep_learning.KerasDeepConvNet.set_fit_request">(moabb.pipelines.deep_learning.KerasDeepConvNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#moabb.pipelines.deep_learning.KerasEEGITNet.set_fit_request">(moabb.pipelines.deep_learning.KerasEEGITNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_fit_request">(moabb.pipelines.deep_learning.KerasEEGNet_8_2 method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#moabb.pipelines.deep_learning.KerasEEGNeX.set_fit_request">(moabb.pipelines.deep_learning.KerasEEGNeX method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#moabb.pipelines.deep_learning.KerasEEGTCNet.set_fit_request">(moabb.pipelines.deep_learning.KerasEEGTCNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#moabb.pipelines.deep_learning.KerasShallowConvNet.set_fit_request">(moabb.pipelines.deep_learning.KerasShallowConvNet method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.set_log_level.html#moabb.set_log_level">set_log_level() (in module moabb)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#moabb.pipelines.deep_learning.KerasDeepConvNet.set_partial_fit_request">set_partial_fit_request() (moabb.pipelines.deep_learning.KerasDeepConvNet method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#moabb.pipelines.deep_learning.KerasEEGITNet.set_partial_fit_request">(moabb.pipelines.deep_learning.KerasEEGITNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_partial_fit_request">(moabb.pipelines.deep_learning.KerasEEGNet_8_2 method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#moabb.pipelines.deep_learning.KerasEEGNeX.set_partial_fit_request">(moabb.pipelines.deep_learning.KerasEEGNeX method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#moabb.pipelines.deep_learning.KerasEEGTCNet.set_partial_fit_request">(moabb.pipelines.deep_learning.KerasEEGTCNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#moabb.pipelines.deep_learning.KerasShallowConvNet.set_partial_fit_request">(moabb.pipelines.deep_learning.KerasShallowConvNet method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA.set_score_request">set_score_request() (moabb.pipelines.classification.SSVEP_CCA method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA.set_score_request">(moabb.pipelines.classification.SSVEP_MsetCCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.set_score_request">(moabb.pipelines.classification.SSVEP_TRCA method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#moabb.pipelines.deep_learning.KerasDeepConvNet.set_score_request">(moabb.pipelines.deep_learning.KerasDeepConvNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#moabb.pipelines.deep_learning.KerasEEGITNet.set_score_request">(moabb.pipelines.deep_learning.KerasEEGITNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#moabb.pipelines.deep_learning.KerasEEGNet_8_2.set_score_request">(moabb.pipelines.deep_learning.KerasEEGNet_8_2 method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#moabb.pipelines.deep_learning.KerasEEGNeX.set_score_request">(moabb.pipelines.deep_learning.KerasEEGNeX method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#moabb.pipelines.deep_learning.KerasEEGTCNet.set_score_request">(moabb.pipelines.deep_learning.KerasEEGTCNet method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#moabb.pipelines.deep_learning.KerasShallowConvNet.set_score_request">(moabb.pipelines.deep_learning.KerasShallowConvNet method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.setup_seed.html#moabb.setup_seed">setup_seed() (in module moabb)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Shin2017A.html#moabb.datasets.Shin2017A">Shin2017A (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Shin2017B.html#moabb.datasets.Shin2017B">Shin2017B (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.SinglePass.html#moabb.paradigms.SinglePass">SinglePass (class in moabb.paradigms)</a>
+
+      <ul>
+        <li><a href="generated/moabb.paradigms.motor_imagery.SinglePass.html#moabb.paradigms.motor_imagery.SinglePass">(class in moabb.paradigms.motor_imagery)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019">Sosulski2019 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP">SSVEP (class in moabb.paradigms)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA">SSVEP_CCA (class in moabb.pipelines.classification)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA">SSVEP_MsetCCA (class in moabb.pipelines.classification)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA">SSVEP_TRCA (class in moabb.pipelines.classification)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.features.StandardScaler_Epoch.html#moabb.pipelines.features.StandardScaler_Epoch">StandardScaler_Epoch (class in moabb.pipelines.features)</a>
+</li>
+      <li><a href="generated/moabb.analysis.plotting.summary_plot.html#moabb.analysis.plotting.summary_plot">summary_plot() (in module moabb.analysis.plotting)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.utils_deep_model.TCN_block.html#moabb.pipelines.utils_deep_model.TCN_block">TCN_block() (in module moabb.pipelines.utils_deep_model)</a>
+</li>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.templates_">templates_ (moabb.pipelines.classification.SSVEP_TRCA attribute)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Thielen2015.html#moabb.datasets.Thielen2015">Thielen2015 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Thielen2021.html#moabb.datasets.Thielen2021">Thielen2021 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal.transform">transform() (moabb.pipelines.features.ExtendedSSVEPSignal method)</a>
+
+      <ul>
+        <li><a href="generated/moabb.pipelines.features.FM.html#moabb.pipelines.features.FM.transform">(moabb.pipelines.features.FM method)</a>
+</li>
+        <li><a href="generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance.transform">(moabb.pipelines.features.LogVariance method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/moabb.pipelines.csp.TRCSP.html#moabb.pipelines.csp.TRCSP">TRCSP (class in moabb.pipelines.csp)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP.used_events">used_events() (moabb.paradigms.ssvep.BaseSSVEP method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Wang2016.html#moabb.datasets.Wang2016">Wang2016 (class in moabb.datasets)</a>
+</li>
+      <li><a href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014">Weibo2014 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA.weights_">weights_ (moabb.pipelines.classification.SSVEP_TRCA attribute)</a>
+</li>
+      <li><a href="generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation">WithinSessionEvaluation (class in moabb.evaluations)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Z">Z</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016">Zhou2016 (class in moabb.datasets)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+                </article>
+              
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+              
+                <footer class="bd-footer-article">
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diff --git a/docs/index.html b/docs/index.html
new file mode 100644
index 00000000..05169633
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@@ -0,0 +1,745 @@
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+                      </a>
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+                
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+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
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+                    </li>
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+                        API reference
+                      </a>
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+                      </a>
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+        
+          <div class="navbar-item"><nav class="navbar-nav">
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+     role="heading"
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+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
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+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
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+                
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+                      <a class="nav-link nav-internal" href="overview.html">
+                        Overview
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_tutorials/index.html">
+                        Getting Started
+                      </a>
+                    </li>
+                
+            <div class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
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+                </button>
+                <div class="dropdown-menu">
+                    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Gallery
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="api.html">
+                        API reference
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        Release notes
+                      </a>
+                    </li>
+                
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+            
+  </ul>
+</nav></div>
+        
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+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+`);
+</script></div>
+        
+      </div>
+    
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+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
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+      
+      <main id="main-content" class="bd-main">
+        
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+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section id="p-align-center-mother-of-all-bci-benchmarks-p">
+<h1><span class="raw-html-m2r"><p align="center">Mother of all BCI Benchmarks</p></span><a class="headerlink" href="#p-align-center-mother-of-all-bci-benchmarks-p" title="Permalink to this heading">#</a></h1>
+<p align="center" class="large-text">
+  <img src="https://raw.githubusercontent.com/bruAristimunha/moabb/new_theme/docs/source/_static/moabb_logo.svg" width="400" height="400" style="display: block; margin: auto;" />
+  Build a comprehensive benchmark of popular Brain-Computer Interface (BCI) algorithms applied on an extensive list of freely available EEG datasets.
+</p><section id="disclaimer">
+<h2>Disclaimer<a class="headerlink" href="#disclaimer" title="Permalink to this heading">#</a></h2>
+<p><strong>This is an open science project that may evolve depending on the need of the
+community.</strong></p>
+<a class="reference external image-reference" href="https://doi.org/10.5281/zenodo.10034224"><img alt="DOI" src="https://zenodo.org/badge/DOI/10.5281/zenodo.10034224.svg" /></a>
+<a class="reference external image-reference" href="https://github.com/NeuroTechX/moabb/actions?query=branch%3Amaster"><img alt="Build Status" src="https://github.com/NeuroTechX/moabb/workflows/Test/badge.svg" /></a>
+<a class="reference external image-reference" href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg" /></a>
+<a class="reference external image-reference" href="https://codecov.io/gh/NeuroTechX/moabb"><img alt="codecov" src="https://codecov.io/gh/NeuroTechX/moabb/graph/badge.svg?token=NwHD3ethB5" /></a>
+<a class="reference external image-reference" href="https://img.shields.io/pypi/v/moabb"><img alt="PyPI" src="https://img.shields.io/pypi/v/moabb?color=blue&amp;style=plastic" /></a>
+<a class="reference external image-reference" href="https://pepy.tech/project/moabb"><img alt="Downloads" src="https://pepy.tech/badge/moabb" /></a>
+</section>
+<section id="welcome">
+<h2>Welcome!<a class="headerlink" href="#welcome" title="Permalink to this heading">#</a></h2>
+<p>Thank you for visiting the Mother of all BCI Benchmark documentation and associated
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">GitHub repository</a></p>
+<p>This document is a hub to give you some information about the project. Jump straight to
+one of the sections below, or just scroll down to find out more.</p>
+<section id="the-problem">
+<h3>The problem<a class="headerlink" href="#the-problem" title="Permalink to this heading">#</a></h3>
+<p><a class="reference external" href="https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface">Brain-Computer Interfaces</a>
+allow to interact with a computer using brain signals. In this project, we focus mostly on
+electroencephalographic signals
+(<a class="reference external" href="https://en.wikipedia.org/wiki/Electroencephalography">EEG</a>), that is a very active
+research domain, with worldwide scientific contributions. Still:</p>
+<ul class="simple">
+<li><p>Reproducible Research in BCI has a long way to go.</p></li>
+<li><p>While many BCI datasets are made freely available, researchers do not publish code, and
+reproducing results required to benchmark new algorithms turns out to be trickier than
+it should be.</p></li>
+<li><p>Performances can be significantly impacted by parameters of the preprocessing steps,
+toolboxes used and implementation “tricks” that are almost never reported in the
+literature.</p></li>
+</ul>
+<p>As a result, there is no comprehensive benchmark of BCI algorithms, and newcomers are
+spending a tremendous amount of time browsing literature to find out what algorithm works
+best and on which dataset.</p>
+</section>
+<section id="the-solution">
+<h3>The solution<a class="headerlink" href="#the-solution" title="Permalink to this heading">#</a></h3>
+<p>The Mother of all BCI Benchmarks allows to:</p>
+<ul class="simple">
+<li><p>Build a comprehensive benchmark of popular BCI algorithms applied on an extensive list
+of freely available EEG datasets.</p></li>
+<li><p>The code is available on GitHub, serving as a reference point for the future algorithmic
+developments.</p></li>
+<li><p>Algorithms can be ranked and promoted on a website, providing a clear picture of the
+different solutions available in the field.</p></li>
+</ul>
+<p>This project will be successful when we read in an abstract “ … the proposed method
+obtained a score of 89% on the MOABB (Mother of All BCI Benchmarks), outperforming the
+state of the art by 5% …”.</p>
+</section>
+<section id="use-moabb">
+<h3>Use MOABB<a class="headerlink" href="#use-moabb" title="Permalink to this heading">#</a></h3>
+<p>First, you could take a look at our <a class="reference external" href="./auto_tutorials/index.html">tutorials</a> that cover
+the most important concepts and use cases. Also, we have a gallery of
+<a class="reference external" href="./auto_examples/index.html">examples</a> available.</p>
+</section>
+</section>
+<section id="core-team">
+<h2>Core Team<a class="headerlink" href="#core-team" title="Permalink to this heading">#</a></h2>
+<p>This project is under the umbrella of <a class="reference external" href="http://neurotechx.com/">NeuroTechX</a>, the international
+community for NeuroTech enthusiasts.</p>
+<p>The project is currently maintained by:</p>
+<table style="text-align: center;">
+  <thead>
+    <tr>
+      <th>Sylvain Chevallier</th>
+      <th>Bruno Aristimunha</th>
+      <th>Igor Carrara</th>
+      <th>Pierre Guetschel</th>
+      <th>Sara Sedlar</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/5824988?s=150&amp;v=4" alt="Sylvain Chevallier"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/42702466?s=150&amp;v=4" alt="Bruno Aristimunha"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/94047258?s=150&amp;v=4" alt="Igor Carrara"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/25532709?s=150&amp;v=4" alt="Pierre Guetschel"></td>
+      <td style="padding: 0 7px;"><img src="https://avatars.githubusercontent.com/u/5344945?s=150&amp;v=4" alt="Sara Sedlar"></td>
+</tr>
+  </tbody>
+</table><p>The Mother of all BCI Benchmarks was founded by Alexander Barachant and Vinay Jayaram, who
+are experts in the field of Brain-Computer Interfaces (BCI). At moment, both works as
+Research Scientist</p>
+<table style="text-align: center;">
+  <thead>
+    <tr>
+      <th>Alexander Barachant</th>
+      <th>Vinay Jayaram</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td style="padding: 0 15px;"><img src="http://alexandre.barachant.org/images/avatar.jpg" alt="Alexander Barachant" width="150" height="150"></td>
+<td style="padding: 0 15px;"><img src="https://beetl.ai/static/media/vinay.217f36bc.jpeg" alt="Vinay Jayaram" width="150" height="150"></td></tr>
+  </tbody>
+</table></section>
+<section id="contributors">
+<h2>Contributors<a class="headerlink" href="#contributors" title="Permalink to this heading">#</a></h2>
+<p>The MOABB is a community project, and we are always thankful for all the contributors!</p>
+<div id="contributors-container"></div><script>
+const endpoint = 'https://api.github.com/repos/NeuroTechX/moabb/contributors';
+const container = document.getElementById('contributors-container');
+const filterList = ["bruAristimunha", "sylvchev", "carraraig", "pierreGtch", "sara04", "pre-commit-ci[bot]", "dependabot[bot]", "alexandrebarachant", "vinay-jayaram"];
+fetch(endpoint)
+  .then(response => response.json())
+  .then(contributors => {
+    const filteredContributors = contributors.filter(contributor => !filterList.includes(contributor.login));    filteredContributors.forEach(contributor => {
+      const link = document.createElement('a');
+      link.href = contributor.html_url;
+      link.target = '_blank';
+      const img = document.createElement('img');
+      img.src = contributor.avatar_url;
+      img.alt = contributor.login;
+      img.style.width = '100px';
+      img.style.height = '100px';
+      img.style.objectFit = 'cover';
+      img.style.borderRadius = '50%';
+      link.appendChild(img);
+      container.appendChild(link);
+    });
+  });
+</script><div id="contributors-container"></div><p>Special acknowledge for the extra MOABB contributors:</p>
+<table style="text-align: center;">
+  <thead>
+    <tr>
+      <th>Pedro Rodrigues</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td style="padding: 0 15px;"><img src="https://avatars.githubusercontent.com/u/4588557?v=4" alt=" Pedro L. C. Rodrigues" width="150" height="150"></td>
+  </tbody>
+</table><section id="what-do-we-need">
+<h3>What do we need?<a class="headerlink" href="#what-do-we-need" title="Permalink to this heading">#</a></h3>
+<p><strong>You</strong>! In whatever way you can help.</p>
+<p>We need expertise in programming, user experience, software sustainability, documentation
+and technical writing and project management.</p>
+<p>We’d love your feedback along the way.</p>
+<p>Our primary goal is to build a comprehensive benchmark of popular BCI algorithms applied
+on an extensive list of freely available EEG datasets, and we’re excited to support the
+professional development of any and all of our contributors. If you’re looking to learn to
+code, try out working collaboratively, or translate your skills to the digital domain,
+we’re here to help.</p>
+</section>
+</section>
+<section id="citing-moabb-and-related-publications">
+<h2>Citing MOABB and related publications<a class="headerlink" href="#citing-moabb-and-related-publications" title="Permalink to this heading">#</a></h2>
+<p>If you use MOABB in your experiments, please cite this library when
+publishing a paper to increase the visibility of open science initiatives:</p>
+<blockquote>
+<div><p>Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Quentin, B., Schirrmeister, R. T.,Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., Roy, Y., Jayaram, V., Barachant,A., &amp; Chevallier, S.
+Mother of all BCI Benchmarks (MOABB), 2023. DOI: 10.5281/zenodo.10034223.</p>
+</div></blockquote>
+<p>and here is the Bibtex version:</p>
+<blockquote>
+<div><p>&#64;software{Aristimunha_Mother_of_all_2023,
+author = {Aristimunha, Bruno and Carrara, Igor and Guetschel, Pierre and Sedlar, Sara and Rodrigues, Pedro and Sosulski, Jan and Narayanan, Divyesh and Bjareholt, Erik and Quentin, Barthelemy and Schirrmeister, Robin Tibor and Kalunga, Emmanuel and Darmet, Ludovic and Gregoire, Cattan and Abdul Hussain, Ali and Gatti, Ramiro and Goncharenko, Vladislav and Thielen, Jordy and Moreau, Thomas and Roy, Yannick and Jayaram, Vinay and Barachant, Alexandre and Chevallier, Sylvain},
+doi = {10.5281/zenodo.10034223},
+title = {{Mother of all BCI Benchmarks}},
+url = {<a class="github reference external" href="https://github.com/NeuroTechX/moabb">NeuroTechX/moabb</a>},
+version = {1.0.0},
+year = {2023}
+}</p>
+</div></blockquote>
+<p>If you want to cite the scientific contributions of MOABB, you could use the following paper:</p>
+<blockquote>
+<div><p>Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Junqueira Lopes, Sébastien Velut, Salim Khazem, Thomas Moreau
+<a class="reference external" href="https://cnrs.hal.science/hal-04537061/">“The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark”</a>
+HAL: hal-04537061.</p>
+<p>Vinay Jayaram and Alexandre Barachant.
+<a class="reference external" href="http://iopscience.iop.org/article/10.1088/1741-2552/aadea0/meta">“MOABB: trustworthy algorithm benchmarking for BCIs.”</a>
+Journal of neural engineering 15.6 (2018): 066011.
+<a class="reference external" href="https://doi.org/10.1088/1741-2552/aadea0">DOI</a></p>
+</div></blockquote>
+<p>If you publish a paper using MOABB, please contact us on <a class="reference external" href="https://app.gitter.im/#/room/#moabb_dev_community:gitter.im">gitter</a> or open an
+issue, and we will add your paper to the
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/wiki/MOABB-bibliography">dedicated wiki page</a>.</p>
+</section>
+<section id="contact-us">
+<h2>Contact us<a class="headerlink" href="#contact-us" title="Permalink to this heading">#</a></h2>
+<p>If you want to report a problem or suggest an enhancement, we’d love for you to
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues">open an issue</a> at this GitHub repository
+because then we can get right on it.</p>
+<p>For a less formal discussion or exchanging ideas, you can also reach us on the <a class="reference external" href="https://app.gitter.im/#/room/#moabb_dev_community:gitter.im">Gitter
+channel</a> or join our weekly office hours! This an open video meeting
+happening on a <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/191">regular basis</a>, please ask
+the link on the gitter channel. We are also on NeuroTechX Slack channel
+<a class="reference external" href="https://neurotechx.com/">#moabb</a>.</p>
+</section>
+</section>
+<section id="id1">
+<h1>Citing MOABB and related publications<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h1>
+<p>If you use MOABB in your experiments, please cite this library when
+publishing a paper to increase the visibility of open science initiatives:</p>
+<ul class="simple">
+<li><p>Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Quentin, B., Schirrmeister, R. T.,Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., Roy, Y., Jayaram, V., Barachant,A., &amp; Chevallier, S.  Mother of all BCI Benchmarks (MOABB), 2023. DOI: <a class="reference external" href="https://www.doi.org/10.5281/zenodo.10034223">10.5281/zenodo.10034223</a></p></li>
+</ul>
+<p>and here is the Bibtex version:</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@software</span><span class="p">{</span><span class="nl">Aristimunha_Mother_of_all_2023</span><span class="p">,</span>
+<span class="w">  </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Aristimunha, Bruno and Carrara, Igor and Guetschel, Pierre and Sedlar, Sara and Rodrigues, Pedro and Sosulski, Jan and Narayanan, Divyesh and Bjareholt, Erik and Quentin, Barthelemy and Schirrmeister, Robin Tibor and Kalunga, Emmanuel and Darmet, Ludovic and Gregoire, Cattan and Abdul Hussain, Ali and Gatti, Ramiro and Goncharenko, Vladislav and Thielen, Jordy and Moreau, Thomas and Roy, Yannick and Jayaram, Vinay and Barachant, Alexandre and Chevallier, Sylvain}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">doi</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{10.5281/zenodo.10034223}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{{Mother of all BCI Benchmarks}}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://github.com/NeuroTechX/moabb}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">version</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1.0.0}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2023}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If you want to cite the scientific contributions of MOABB, you could use the following paper:</p>
+<ul class="simple">
+<li><p>Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Junqueira Lopes, Sébastien Velut, Salim Khazem, Thomas Moreau. <a class="reference external" href="https://cnrs.hal.science/hal-04537061/">The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark</a>, 2023. HAL: hal-04537061.</p></li>
+<li><p>Vinay Jayaram and Alexandre Barachant. MOABB: trustworthy algorithm
+benchmarking for BCIs. Journal of neural engineering 15.6 (2018):
+066011. <a class="reference external" href="https://doi.org/10.1088/1741-2552/aadea0">DOI:10.1088/1741-2552</a></p></li>
+</ul>
+<p>Here is the BibTeX entry for the above paper:</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="p">{</span><span class="nl">moabb2018</span><span class="p">,</span>
+<span class="w">  </span><span class="na">title</span><span class="p">=</span><span class="s">{MOABB: trustworthy algorithm benchmarking for BCIs}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">author</span><span class="p">=</span><span class="s">{Jayaram, Vinay and Barachant, Alexandre}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">journal</span><span class="p">=</span><span class="s">{Journal of neural engineering}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">volume</span><span class="p">=</span><span class="s">{15}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">number</span><span class="p">=</span><span class="s">{6}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">pages</span><span class="p">=</span><span class="s">{066011}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">year</span><span class="p">=</span><span class="s">{2018}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">publisher</span><span class="p">=</span><span class="s">{IOP Publishing}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If you publish a paper using MOABB, please contact us on
+<a class="reference external" href="https://app.gitter.im/#/room/#moabb_dev_community:gitter.im">gitter</a>
+or open an issue, and we will add your paper to the <a class="reference external" href="https://github.com/NeuroTechX/moabb/wiki/MOABB-bibliography">dedicated wiki
+page</a>.</p>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+
+
+                </article>
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@@ -0,0 +1,614 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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+    
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+                      <a class="nav-link nav-internal" href="../paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="../dataset_summary.html">
+                        Datasets
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+  <section id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h1>
+<p>MOABB is written in Python 3, specifically for version 3.8 or above.</p>
+<p>The package is distributed via Python package index (<a class="reference external" href="https://pypi.org/project/moabb">PyPI</a>), and you can access the
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+<p>There are different ways to install MOABB:</p>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
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+</div>
+<div class="sd-card-body docutils">
+<img alt="Terminal Window" src="https://mne.tools/stable/_images/mne_installer_console.png" />
+<p class="sd-card-text"><strong>Already familiar with Python?</strong>
+Follow our setup instructions for building from Github and start to contribute!</p>
+</div>
+<div class="sd-card-footer docutils">
+<p class="sd-card-text"><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-primary sd-shadow-sm font-weight-bold reference internal" href="install_source.html#install-source"><span class="std std-ref">From src and dev env</span></a></p>
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+<div class="sd-card-header docutils">
+<p class="font-weight-bold mb-0 sd-card-text">Using our docker environment</p>
+<p class="card-subtitle text-muted mt-0 sd-card-text">For Advanced Users</p>
+</div>
+<div class="sd-card-body docutils">
+<img alt="Terminal Docker" src="https://www.docker.com/wp-content/uploads/2022/03/Moby-logo.png" />
+<p class="sd-card-text"><strong>Already familiar with Docker?</strong>
+Follow our setup instructions for using your docker image!</p>
+</div>
+<div class="sd-card-footer docutils">
+<p class="sd-card-text"><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-primary sd-shadow-sm font-weight-bold reference internal" href="using_docker.html#using-docker"><span class="std std-ref">Using the docker image</span></a></p>
+</div>
+</div>
+</div>
+</div>
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diff --git a/docs/install/install_pip.html b/docs/install/install_pip.html
new file mode 100644
index 00000000..a7f54b3e
--- /dev/null
+++ b/docs/install/install_pip.html
@@ -0,0 +1,585 @@
+
+
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+                  
+  <section id="installing-from-pypi">
+<span id="install-pip"></span><h1>Installing from PyPI<a class="headerlink" href="#installing-from-pypi" title="Permalink to this heading">#</a></h1>
+<p>MOABB can be installed via pip from <a class="reference external" href="https://pypi.org/project/moabb">PyPI</a>.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>MOABB is only compatible with <strong>Python 3.8, 3.9, 3.10 and 3.11</strong>.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We recommend the most updated version of pip to install from PyPI.</p>
+</div>
+<p>Below are the installation commands for the most common use cases.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">pip install bash</span>
+</pre></div>
+</div>
+<p>MOABB can also be installed with sets of optional dependencies to enable the deep learning modules, the tracker of the carbon emission module or the documentation environment.</p>
+<p>For example, to install all the optional dependencies.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>moabb<span class="o">[</span>deepleaning,<span class="w"> </span>carbonemission,<span class="w"> </span>docs<span class="o">]</span>
+</pre></div>
+</div>
+<p>If you install the deep learning submodule using the pip, the installation commands above usually install a CPU variant of PyTorch or Tensorflow.</p>
+<p>To use the potential of the deep learning modules (TensorFlow or PyTorch) with GPU, we recommend the following sequence before installing the moabb with dependencies:</p>
+<ol class="arabic simple">
+<li><p>Install the latest NVIDIA driver.</p></li>
+<li><p>Check <a class="reference external" href="https://pytorch.org/get-started/locally/">PyTorch Official Guide</a> or <a class="reference external" href="https://www.tensorflow.org/install/gpu">TensorFlow Official Guide</a>, for the recommended CUDA versions. For the Pip package, the user must download the CUDA manually, install it on the system, and ensure CUDA_PATH is appropriately set and working!</p></li>
+<li><p>Continue to follow the guide and install PyTorch or TensorFlow.</p></li>
+<li><p>Install moabb using one of the ways with the dependency parameter <a href="#id1"><span class="problematic" id="id2">`</span></a>[deeplearning].</p></li>
+</ol>
+<p>See <a class="reference external" href="moabb.Troubleshooting.com">Troubleshooting</a> section if you have a problem.</p>
+</section>
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new file mode 100644
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+                <article class="bd-article" role="main">
+                  
+  <section id="installing-from-sources">
+<span id="install-source"></span><h1>Installing from sources<a class="headerlink" href="#installing-from-sources" title="Permalink to this heading">#</a></h1>
+<p>If you want to test features under development or contribute to the library, or if you want to test the new tools that have been tested in moabb and not released yet, this is the right tutorial for you!</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are only trying to install MOABB, we recommend using the pip installation <a class="reference external" href="https://neurotechx.github.io/moabb/install/install_pip.html#install-pip">Installation</a> for details on that.</p>
+</div>
+<section id="clone-the-repository-from-github">
+<span id="system-level"></span><h2>Clone the repository from GitHub<a class="headerlink" href="#clone-the-repository-from-github" title="Permalink to this heading">#</a></h2>
+<p>The first thing you should do is clone the MOABB repository to your computer and enter inside the repository.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/neurotechx/moabb<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>moabb
+</pre></div>
+</div>
+<p>You should now be in the root directory of the MOABB repository.</p>
+</section>
+<section id="installing-moabb-from-the-source">
+<h2>Installing Moabb from the source<a class="headerlink" href="#installing-moabb-from-the-source" title="Permalink to this heading">#</a></h2>
+<p>If you want to only install Moabb from source once and not do any development
+work, then the recommended way to build and install is to use <code class="docutils literal notranslate"><span class="pre">pip</span></code>:</p>
+<p>For the latest development version, directly from GitHub:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>https://github.com/NeuroTechX/moabb/archive/refs/heads/develop.zip
+</pre></div>
+</div>
+<p>If you have a local clone of the MOABB git repository:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>.
+</pre></div>
+</div>
+<p>You can also install MOABB in editable mode (i.e. changes to the source code).</p>
+</section>
+<section id="building-moabb-from-source-with-the-development-environment">
+<h2>Building MOABB from source with the development environment<a class="headerlink" href="#building-moabb-from-source-with-the-development-environment" title="Permalink to this heading">#</a></h2>
+<p>If you want to build from source to work on MOABB itself, then follow these steps:</p>
+<ol class="arabic simple">
+<li><p>Install poetry by running the following command (only needs to be done once per machine):</p></li>
+</ol>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>curl<span class="w"> </span>-sSL<span class="w"> </span>https://install.python-poetry.org<span class="w"> </span><span class="p">|</span><span class="w"> </span>python3<span class="w"> </span>-
+</pre></div>
+</div>
+<p>You could also check checkout <a class="reference external" href="https://python-poetry.org/docs/#installation">poetry installation instruction</a> or
+use <a class="reference external" href="https://anaconda.org/conda-forge/poetry">conda forge version</a></p>
+<p>We need the most updated version of the poetry to ensure the compatibility with optional dependency.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you have any group-related errors at the end of this section, you may not run the proper version of poetry.</p>
+</div>
+<ol class="arabic simple" start="2">
+<li><p>(Optional) We recommend disabling automatic environment creation:</p></li>
+</ol>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">poetry config virtualenvs.create false</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This step is optional. Skip if you are not sure.</p>
+</div>
+<ol class="arabic simple" start="3">
+<li><p>If you want to install without any optional dependency</p></li>
+</ol>
+<p>You will need to run this command in the project directory:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">poetry install</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="4">
+<li><p>If you want to install with an optional dependency</p></li>
+</ol>
+<p>You will need to run this command in the project directory:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">poetry install --extras &quot;deeplearning carbonemission docs&quot;</span>
+</pre></div>
+</div>
+<p>For a full list of dependencies, see the pyproject.toml file.</p>
+<p>To contribute with a library you must install <code class="docutils literal notranslate"><span class="pre">pre-commit</span></code>, follow this tutorial   <a class="reference external" href="https://pre-commit.com/#install">Installation Pre-Commit</a>. To more details to become a contributors, see
+<a class="reference external" href="https://github.com/NeuroTechX/moabb/blob/master/CONTRIBUTING.md">contributors’ guidelines</a>.
+for a detailed explanation.</p>
+</section>
+</section>
+<section id="testing-if-your-installation-is-working">
+<h1>Testing if your installation is working<a class="headerlink" href="#testing-if-your-installation-is-working" title="Permalink to this heading">#</a></h1>
+<p>To verify that MOABB is installed and running correctly, run the following command:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python -m unittest moabb.tests</span>
+</pre></div>
+</div>
+<p>For more information, please see the contributors’ guidelines.</p>
+</section>
+
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+                  
+  <section id="moabb-and-docker">
+<span id="using-docker"></span><h1>Moabb and docker<a class="headerlink" href="#moabb-and-docker" title="Permalink to this heading">#</a></h1>
+<section id="if-you-want-to-use-the-docker-image-pre-build">
+<h2>If you want to use the docker image pre-build<a class="headerlink" href="#if-you-want-to-use-the-docker-image-pre-build" title="Permalink to this heading">#</a></h2>
+<p>Moabb has a default image to run the benchmark. You have two options to
+download this image: build from scratch or pull from the docker hub.
+<strong>We recommend pulling from the docker hub</strong>.</p>
+<p>If this were your first time using docker, you would need to <strong>install
+the docker</strong> and <strong>login</strong> on docker hub. We recommend the
+<a class="reference external" href="https://docs.docker.com/desktop/install/linux-install/">official</a>
+docker documentation for this step, it is essential to follow the
+instructions.</p>
+<p>After installing docker, you can pull the image from the docker hub:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>pull<span class="w"> </span>baristimunha/moabb
+<span class="c1"># rename the tag to moabb</span>
+docker<span class="w"> </span>tag<span class="w"> </span>baristimunha/moabb<span class="w"> </span>moabb
+</pre></div>
+</div>
+<p>If you want to build the image from scratch, you can use the following
+command at the root. You may have to login with the API key in the <a class="reference external" href="https://catalog.ngc.nvidia.com/">NGC
+Catalog</a> to run this command.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>bash<span class="w"> </span>docker/create_docker.sh
+</pre></div>
+</div>
+<p>With the image downloaded or rebuilt from scratch, you will have an
+image called <code class="docutils literal notranslate"><span class="pre">moabb</span></code>. To run the default benchmark, still at the root
+of the project, and you can use the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mkdir<span class="w"> </span>dataset
+mkdir<span class="w"> </span>results
+mkdir<span class="w"> </span>output
+bash<span class="w"> </span>docker/run_docker.sh<span class="w"> </span>PATH_TO_ROOT_FOLDER
+</pre></div>
+</div>
+<p>An example of the command is:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>/home/user/project/moabb
+mkdir<span class="w"> </span>dataset
+mkdir<span class="w"> </span>results
+mkdir<span class="w"> </span>output
+bash<span class="w"> </span>docker/run_docker.sh<span class="w"> </span>/home/user/project/moabb
+</pre></div>
+</div>
+<p>Note: It is important to use an absolute path for the root folder to
+run, but you can modify the run_docker.sh script to save in another path
+beyond the root of the project. By default, the script will save the
+results in the project’s root in the folder <code class="docutils literal notranslate"><span class="pre">results</span></code>, the datasets in
+the folder <code class="docutils literal notranslate"><span class="pre">dataset</span></code> and the output in the folder <code class="docutils literal notranslate"><span class="pre">output</span></code>.</p>
+</section>
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+                  
+  <section id="architecture-and-main-concepts">
+<h1>Architecture and Main Concepts<a class="headerlink" href="#architecture-and-main-concepts" title="Permalink to this heading">#</a></h1>
+<a class="reference external image-reference" href="images/architecture.png"><img alt="architecture" src="_images/architecture.png" /></a>
+<p>There are 4 main concepts in the MOABB: the datasets, the paradigm, the evaluation, and
+the pipelines. In addition, we offer statistical and visualization utilities to simplify
+the workflow.</p>
+<section id="datasets">
+<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this heading">#</a></h2>
+<p>A dataset handles and abstracts low-level access to the data. The dataset will read data
+stored locally, in the format in which they have been downloaded, and will convert them
+into an MNE raw object. There are options to pool all the different recording sessions per
+subject or to evaluate them separately.</p>
+</section>
+<section id="paradigm">
+<h2>Paradigm<a class="headerlink" href="#paradigm" title="Permalink to this heading">#</a></h2>
+<p>A paradigm defines how the raw data will be converted to trials ready to be processed by a
+decoding algorithm. This is a function of the paradigm used, i.e. in motor imagery one can
+have two-class, multi-class, or continuous paradigms; similarly, different preprocessing
+is necessary for ERP vs ERD paradigms.</p>
+</section>
+<section id="evaluations">
+<h2>Evaluations<a class="headerlink" href="#evaluations" title="Permalink to this heading">#</a></h2>
+<p>An evaluation defines how we go from trials per subject and session to a generalization
+statistic (AUC score, f-score, accuracy, etc) – it can be either within-recording-session
+accuracy, across-session within-subject accuracy, across-subject accuracy, or other
+transfer learning settings.</p>
+</section>
+<section id="pipelines">
+<h2>Pipelines<a class="headerlink" href="#pipelines" title="Permalink to this heading">#</a></h2>
+<p>Pipeline defines all steps required by an algorithm to obtain predictions. Pipelines are
+typically a chain of sklearn compatible transformers and end with a sklearn compatible
+estimator. See
+<a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html">Pipelines</a>
+for more info.</p>
+</section>
+<section id="statistics-and-visualization">
+<h2>Statistics and visualization<a class="headerlink" href="#statistics-and-visualization" title="Permalink to this heading">#</a></h2>
+<p>Once an evaluation has been run, the raw results are returned as a DataFrame. This can be
+further processed via the following commands to generate some basic visualization and
+statistical comparisons:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">moabb.analysis</span> <span class="kn">import</span> <span class="n">analyze</span>
+
+<span class="n">results</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">process</span><span class="p">(</span><span class="n">pipeline_dict</span><span class="p">)</span>
+<span class="n">analyze</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#datasets">Datasets</a></li>
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new file mode 100644
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+    <title>Documentation overview &#8212; moabb 1.0.0-dev documentation</title>
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+    
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+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
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+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
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+                        Install
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l2"><a class="reference internal" href="auto_tutorials/4_adding_a_dataset.html">Tutorial 4: Creating a dataset class</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_tutorials/noplot_tutorial_5_build_a_custom_dataset.html">Tutorial 5: Creating a dataset class</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_tutorials/plot_Getting_Started.html">Tutorial 0: Getting Started</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_tutorials/tutorial_1_simple_example_motor_imagery.html">Tutorial 1: Simple Motor Imagery</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_tutorials/tutorial_2_using_mulitple_datasets.html">Tutorial 2: Using multiple datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_tutorials/tutorial_3_benchmarking_multiple_pipelines.html">Tutorial 3: Benchmarking multiple pipelines</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="auto_examples/index.html">Examples</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="auto_examples/changing_download_directory.html">Change Download Directory</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_examples/example_codecarbon.html">Benchmarking with MOABB showing the CO2 footprint</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_examples/load_model.html">Load Model (Scikit, Pytorch, Keras) with MOABB</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_examples/noplot_bids_conversion.html">Convert a MOABB dataset to BIDS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_examples/noplot_phmd_ml_spectrum.html">Spectral analysis of the trials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="auto_examples/noplot_vr_pc_p300_different_epoch_size.html">Changing epoch size in P300 VR dataset</a></li>
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+<div style="font-size: 1.5em;"><section id="the-largest-eeg-based-benchmark-for-open-science">
+<h1>The largest EEG-based Benchmark for Open Science<a class="headerlink" href="#the-largest-eeg-based-benchmark-for-open-science" title="Permalink to this heading">#</a></h1>
+<p>We report the results of the benchmark study performed in:
+<a class="reference external" href="https://universite-paris-saclay.hal.science/hal-04537061v1/file/MOABB-arXiv.pdf">The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark</a></p>
+<p>This study conducts an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets,
+aiming to assess existing solutions and establish open and reproducible benchmarks for effective comparison within the field. Please note that the results are obtained using <a class="reference external" href="http://moabb.neurotechx.com/docs/generated/moabb.evaluations.WithinSessionEvaluation.html">Within-Session evaluation</a>.
+The results are reported regarding mean accuracy and standard deviation across all folds for all sessions and subjects.</p>
+<p>If you use the same evaluation procedure, you should expect similar results if you use the same pipelines and datasets, with some minor variations due to the randomness of the cross-validation procedure.</p>
+<p><strong>You can copy and use the table in your work</strong>, but please <a class="reference external" href="http://moabb.neurotechx.com/docs/cite.html">**cite the paper**</a> if you do so.</p>
+</section>
+<section id="motor-imagery">
+<h1>Motor Imagery<a class="headerlink" href="#motor-imagery" title="Permalink to this heading">#</a></h1>
+<p>Motor Imagery is a BCI paradigm where the subject imagines performing a movement.
+Each imagery task is associated with a different class, and each task has its difficulty level related to how the brain generates the signal.</p>
+<p>Here, we present three different scenarios for Motor Imagery classification:</p>
+<ol class="arabic simple">
+<li><p><strong>Left vs Right Hand</strong>: We use only the classes Left Hand and Right Hand.</p></li>
+<li><p><strong>Right Hand vs Feet</strong>: We use only Right Hand and Feet classes.</p></li>
+<li><p><strong>All classes</strong>: We use all the classes in the dataset, when there are more than classes that are not Left Hand and Right Hand.</p></li>
+</ol>
+<p>All the results here are for <strong>within-session evaluation</strong>, a 5-fold cross-validation, over the subject’s session.</p>
+</section>
+<section id="motor-imagery-left-vs-right-hand">
+<h1>Motor Imagery - Left vs Right Hand<a class="headerlink" href="#motor-imagery-left-vs-right-hand" title="Permalink to this heading">#</a></h1>
+<p><strong>Left vs Right Hand</strong>: We use only the classes Left Hand and Right Hand.</p>
+  <table id="mileftvsright" class="hover row-border order-column" style="width:100%">
+      <thead>
+      <tr class="row-odd"><th class="head"><p>Pipelines</p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_004.html#moabb.datasets.BNCI2014_004" title="moabb.datasets.BNCI2014_004"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_004</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Cho2017.html#moabb.datasets.Cho2017" title="moabb.datasets.Cho2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cho2017</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.GrosseWentrup2009.html#moabb.datasets.GrosseWentrup2009" title="moabb.datasets.GrosseWentrup2009"><code class="xref py py-class docutils literal notranslate"><span class="pre">GrosseWentrup2009</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_MI.html#moabb.datasets.Lee2019_MI" title="moabb.datasets.Lee2019_MI"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lee2019_MI</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017" title="moabb.datasets.Schirrmeister2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Shin2017A.html#moabb.datasets.Shin2017A" title="moabb.datasets.Shin2017A"><code class="xref py py-class docutils literal notranslate"><span class="pre">Shin2017A</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014" title="moabb.datasets.Weibo2014"><code class="xref py py-class docutils literal notranslate"><span class="pre">Weibo2014</span></code></a></p></th>
+      <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Zhou2016</span></code></a></p></th>
+      </tr>
+      </thead>
+  </table>
+  <script type="text/javascript">
+      $(document).ready(function() {
+         $('#mileftvsright').DataTable( {
+            "ajax": 'https://raw.githubusercontent.com/bruAristimunha/moabb/table_results/results/within_session_mi_left_vs_right_hand.json',
+            "order": [[ 1, "desc" ]],
+            "bJQueryUI": true,
+            "scrollX": true,
+            "paging": false,
+            "info": false,
+            "searching": false,
+              layout: {
+                      topStart: {
+                          buttons: ['copyHtml5']
+                      }
+         } );
+      } );
+ </script>
+ <hr></section>
+<section id="motor-imagery-right-hand-vs-feet">
+<h1>Motor Imagery - Right Hand vs Feet<a class="headerlink" href="#motor-imagery-right-hand-vs-feet" title="Permalink to this heading">#</a></h1>
+<p><strong>Right Hand vs Feet</strong>: We use only Right Hand and Feet classes.</p>
+ <table id="mirightvsfeet" class="hover row-border order-column" style="width:100%">
+     <thead>
+     <tr class="row-odd"><th class="head"><p>Pipeline</p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">AlexMI</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_002.html#moabb.datasets.BNCI2014_002" title="moabb.datasets.BNCI2014_002"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_002</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_001.html#moabb.datasets.BNCI2015_001" title="moabb.datasets.BNCI2015_001"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2015_001</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_004.html#moabb.datasets.BNCI2015_004" title="moabb.datasets.BNCI2015_004"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2015_004</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017" title="moabb.datasets.Schirrmeister2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014" title="moabb.datasets.Weibo2014"><code class="xref py py-class docutils literal notranslate"><span class="pre">Weibo2014</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Zhou2016</span></code></a></p></th>
+     </tr>
+     </thead>
+ </table>
+
+
+<script type="text/javascript">
+     $(document).ready(function() {
+        $('#mirightvsfeet').DataTable( {
+           "ajax": 'https://raw.githubusercontent.com/bruAristimunha/moabb/table_results/results/within_session_mi_right_hand_vs_feet.json',
+           "order": [[ 1, "desc" ]],
+           "bJQueryUI": true,
+           "scrollX": true,
+           "paging": false,
+           "info": false,
+           "searching": false,
+        } );
+     } );
+</script></section>
+<section id="motor-imagery-all-classes">
+<h1>Motor Imagery - All classes<a class="headerlink" href="#motor-imagery-all-classes" title="Permalink to this heading">#</a></h1>
+<p><strong>All classes</strong>: We use all the classes in the dataset, when there are more than classes that are not Left Hand and Right Hand.</p>
+ <table id="mi-all" class="hover row-border order-column" style="width:100%">
+     <thead>
+     <tr class="row-odd"><th class="head"><p>Pipelines</p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.AlexMI.html#moabb.datasets.AlexMI" title="moabb.datasets.AlexMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">AlexMI</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_001.html#moabb.datasets.BNCI2014_001" title="moabb.datasets.BNCI2014_001"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">PhysionetMI</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Schirrmeister2017.html#moabb.datasets.Schirrmeister2017" title="moabb.datasets.Schirrmeister2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Schirrmeister2017</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Weibo2014.html#moabb.datasets.Weibo2014" title="moabb.datasets.Weibo2014"><code class="xref py py-class docutils literal notranslate"><span class="pre">Weibo2014</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Zhou2016.html#moabb.datasets.Zhou2016" title="moabb.datasets.Zhou2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Zhou2016</span></code></a></p></th>
+     </tr>
+     </thead>
+ </table>
+ <script type="text/javascript">
+     $(document).ready(function() {
+        $('#mi-all').DataTable( {
+           "ajax": 'https://raw.githubusercontent.com/bruAristimunha/moabb/table_results/results/within_session_mi_all_classes.json',
+           "order": [[ 1, "desc" ]],
+           "bJQueryUI": true,
+           "scrollX": true,
+           "paging": false,
+           "searching": false,
+           "info": false,
+        } );
+     } );
+ </script></section>
+<section id="ssvep-all-classes">
+<h1>SSVEP (All classes)<a class="headerlink" href="#ssvep-all-classes" title="Permalink to this heading">#</a></h1>
+<p>Here, we have the results of the within-session evaluation, a 5-fold cross-validation, over the subject’s session.
+We use all the classes available in the dataset.</p>
+<script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
+<link href="https://cdn.datatables.net/v/dt/dt-2.0.4/b-3.0.2/b-html5-3.0.2/datatables.min.css" rel="stylesheet">
+<script type="text/javascript" charset="utf8" src="https://cdn.datatables.net/v/dt/dt-2.0.4/b-3.0.2/b-html5-3.0.2/datatables.min.js"></script>
+
+ <table id="ssvep" class="hover row-border order-column" style="width:100%">
+     <thead>
+     <tr class="row-odd"><th class="head"><p>Pipeline</p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Kalunga2016.html#moabb.datasets.Kalunga2016" title="moabb.datasets.Kalunga2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kalunga2016</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_SSVEP.html#moabb.datasets.Lee2019_SSVEP" title="moabb.datasets.Lee2019_SSVEP"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lee2019_SSVEP</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.MAMEM1.html#moabb.datasets.MAMEM1" title="moabb.datasets.MAMEM1"><code class="xref py py-class docutils literal notranslate"><span class="pre">MAMEM1</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.MAMEM2.html#moabb.datasets.MAMEM2" title="moabb.datasets.MAMEM2"><code class="xref py py-class docutils literal notranslate"><span class="pre">MAMEM2</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.MAMEM3.html#moabb.datasets.MAMEM3" title="moabb.datasets.MAMEM3"><code class="xref py py-class docutils literal notranslate"><span class="pre">MAMEM3</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Nakanishi2015.html#moabb.datasets.Nakanishi2015" title="moabb.datasets.Nakanishi2015"><code class="xref py py-class docutils literal notranslate"><span class="pre">Nakanishi2015</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Wang2016.html#moabb.datasets.Wang2016" title="moabb.datasets.Wang2016"><code class="xref py py-class docutils literal notranslate"><span class="pre">Wang2016</span></code></a></p></th>
+     </tr>
+     </thead>
+ </table>
+
+
+<script type="text/javascript">
+     $(document).ready(function() {
+        $('#ssvep').DataTable( {
+           "ajax": 'https://raw.githubusercontent.com/bruAristimunha/moabb/table_results/results/within_session_ssvep_all_classes.json',
+           "order": [[ 1, "desc" ]],
+           "bJQueryUI": true,
+           "scrollX": true,
+           "paging": false,
+           "searching": false,
+           "info": false,
+                       "buttons": ["copyHtml5","csvHtml5"],
+        } );
+     } );
+</script></section>
+<section id="p300-erp-all-classes">
+<h1>P300/ERP (All classes)<a class="headerlink" href="#p300-erp-all-classes" title="Permalink to this heading">#</a></h1>
+<p>Here, we have the results of the within-session evaluation, a 5-fold cross-validation, over the subject’s session.
+We use all the classes available in the dataset.</p>
+ <table id="p300" class="hover row-border order-column" style="width:100%">
+     <thead>
+     <tr class="row-odd"><th class="head"><p>Pipelines</p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_008.html#moabb.datasets.BNCI2014_008" title="moabb.datasets.BNCI2014_008"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_008</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2014_009.html#moabb.datasets.BNCI2014_009" title="moabb.datasets.BNCI2014_009"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_009</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BNCI2015_003.html#moabb.datasets.BNCI2015_003" title="moabb.datasets.BNCI2015_003"><code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2015_003</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BI2012.html#moabb.datasets.BI2012" title="moabb.datasets.BI2012"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2012</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BI2013a.html#moabb.datasets.BI2013a" title="moabb.datasets.BI2013a"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2013a</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BI2014a.html#moabb.datasets.BI2014a" title="moabb.datasets.BI2014a"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014a</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BI2014b.html#moabb.datasets.BI2014b" title="moabb.datasets.BI2014b"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2014b</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BI2015a.html#moabb.datasets.BI2015a" title="moabb.datasets.BI2015a"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015a</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.BI2015b.html#moabb.datasets.BI2015b" title="moabb.datasets.BI2015b"><code class="xref py py-class docutils literal notranslate"><span class="pre">BI2015b</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Cattan2019_VR.html#moabb.datasets.Cattan2019_VR" title="moabb.datasets.Cattan2019_VR"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cattan2019_VR</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.EPFLP300.html#moabb.datasets.EPFLP300" title="moabb.datasets.EPFLP300"><code class="xref py py-class docutils literal notranslate"><span class="pre">EPFLP300</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Huebner2017.html#moabb.datasets.Huebner2017" title="moabb.datasets.Huebner2017"><code class="xref py py-class docutils literal notranslate"><span class="pre">Huebner2017</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Huebner2018.html#moabb.datasets.Huebner2018" title="moabb.datasets.Huebner2018"><code class="xref py py-class docutils literal notranslate"><span class="pre">Huebner2018</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Lee2019_ERP.html#moabb.datasets.Lee2019_ERP" title="moabb.datasets.Lee2019_ERP"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lee2019_ERP</span></code></a></p></th>
+     <th class="head"><p><a class="reference internal" href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019" title="moabb.datasets.Sosulski2019"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sosulski2019</span></code></a></p></th>
+     </tr>
+     </thead>
+ </table>
+
+
+<script type="text/javascript">
+     $(document).ready(function() {
+        $('#p300').DataTable( {
+           "ajax": 'https://raw.githubusercontent.com/bruAristimunha/moabb/table_results/results/within_session_erp_p300_all_classes.json',
+           "order": [[ 1, "desc" ]],
+           "bJQueryUI": true,
+           "scrollX": true,
+           "paging": false,
+           "searching": false,
+           "info": false,
+                       "buttons": ["copyHtml5","csvHtml5"],
+        } );
+     } );
+</script>
+<p></p><div class="toctree-wrapper compound">
+</div>
+</section>
+
+
+                </article>
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diff --git a/docs/paradigms.html b/docs/paradigms.html
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--- /dev/null
+++ b/docs/paradigms.html
@@ -0,0 +1,651 @@
+
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+<!DOCTYPE html>
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+<html lang="en" data-theme="light">
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+                      </a>
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+    <li class="breadcrumb-item active" aria-current="page">Paradigms</li>
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+                  
+  <section id="module-moabb.paradigms">
+<span id="paradigms"></span><h1>Paradigms<a class="headerlink" href="#module-moabb.paradigms" title="Permalink to this heading">#</a></h1>
+<p>A paradigm defines how the raw data will be converted to trials ready to be
+processed by a decoding algorithm.</p>
+<p>This is a function of the paradigm used, i.e. in motor imagery one can
+have two-class, multi-class, or continuous paradigms; similarly,
+different preprocessing is necessary for ERP vs ERD paradigms.</p>
+<section id="motor-imagery-paradigms">
+<h2>Motor Imagery Paradigms<a class="headerlink" href="#motor-imagery-paradigms" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.MotorImagery.html#moabb.paradigms.MotorImagery" title="moabb.paradigms.MotorImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MotorImagery</span></code></a>([n_classes])</p></td>
+<td><p>N-class motor imagery.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.LeftRightImagery.html#moabb.paradigms.LeftRightImagery" title="moabb.paradigms.LeftRightImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LeftRightImagery</span></code></a>(**kwargs)</p></td>
+<td><p>Motor Imagery for left hand/right hand classification.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankLeftRightImagery.html#moabb.paradigms.FilterBankLeftRightImagery" title="moabb.paradigms.FilterBankLeftRightImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankLeftRightImagery</span></code></a>(**kwargs)</p></td>
+<td><p>Filter Bank Motor Imagery for left hand/right hand classification.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankMotorImagery.html#moabb.paradigms.FilterBankMotorImagery" title="moabb.paradigms.FilterBankMotorImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankMotorImagery</span></code></a>([n_classes])</p></td>
+<td><p>Filter bank n-class motor imagery.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="p300-paradigms">
+<h2>P300 Paradigms<a class="headerlink" href="#p300-paradigms" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.SinglePass.html#moabb.paradigms.SinglePass" title="moabb.paradigms.SinglePass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SinglePass</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single Bandpass filter P300.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.P300.html#moabb.paradigms.P300" title="moabb.paradigms.P300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">P300</span></code></a>(**kwargs)</p></td>
+<td><p>P300 for Target/NonTarget classification.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="ssvep-paradigms">
+<h2>SSVEP Paradigms<a class="headerlink" href="#ssvep-paradigms" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.SSVEP.html#moabb.paradigms.SSVEP" title="moabb.paradigms.SSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SSVEP</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single bandpass filter SSVEP.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankSSVEP.html#moabb.paradigms.FilterBankSSVEP" title="moabb.paradigms.FilterBankSSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankSSVEP</span></code></a>([filters])</p></td>
+<td><p>Filtered bank n-class SSVEP paradigm.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="c-vep-paradigms">
+<h2>c-VEP Paradigms<a class="headerlink" href="#c-vep-paradigms" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.CVEP.html#moabb.paradigms.CVEP" title="moabb.paradigms.CVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CVEP</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single bandpass c-VEP paradigm for epoch-level decoding.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankCVEP.html#moabb.paradigms.FilterBankCVEP" title="moabb.paradigms.FilterBankCVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankCVEP</span></code></a>([filters])</p></td>
+<td><p>Filterbank c-VEP paradigm for epoch-level decoding.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="fixed-interval-windows-processings">
+<h2>Fixed Interval Windows Processings<a class="headerlink" href="#fixed-interval-windows-processings" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FixedIntervalWindowsProcessing</span></code></a>([fmin, fmax, ...])</p></td>
+<td><p>Fixed interval windows processing.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html#moabb.paradigms.FilterBankFixedIntervalWindowsProcessing" title="moabb.paradigms.FilterBankFixedIntervalWindowsProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FilterBankFixedIntervalWindowsProcessing</span></code></a>([...])</p></td>
+<td><p>Filter bank fixed interval windows processing.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="base-utils">
+<h2>Base &amp; Utils<a class="headerlink" href="#base-utils" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.motor_imagery.BaseMotorImagery.html#moabb.paradigms.motor_imagery.BaseMotorImagery" title="moabb.paradigms.motor_imagery.BaseMotorImagery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">motor_imagery.BaseMotorImagery</span></code></a>([filters, ...])</p></td>
+<td><p>Base Motor imagery paradigm.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.motor_imagery.SinglePass.html#moabb.paradigms.motor_imagery.SinglePass" title="moabb.paradigms.motor_imagery.SinglePass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">motor_imagery.SinglePass</span></code></a>([fmin, fmax])</p></td>
+<td><p>Single Bandpass filter motor Imagery.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.motor_imagery.FilterBank.html#moabb.paradigms.motor_imagery.FilterBank" title="moabb.paradigms.motor_imagery.FilterBank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">motor_imagery.FilterBank</span></code></a>([filters])</p></td>
+<td><p>Filter Bank MI.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.p300.BaseP300.html#moabb.paradigms.p300.BaseP300" title="moabb.paradigms.p300.BaseP300"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p300.BaseP300</span></code></a>([filters, events, tmin, tmax, ...])</p></td>
+<td><p>Base P300 paradigm.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.ssvep.BaseSSVEP.html#moabb.paradigms.ssvep.BaseSSVEP" title="moabb.paradigms.ssvep.BaseSSVEP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ssvep.BaseSSVEP</span></code></a>([filters, events, ...])</p></td>
+<td><p>Base SSVEP Paradigm.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html#moabb.paradigms.BaseFixedIntervalWindowsProcessing" title="moabb.paradigms.BaseFixedIntervalWindowsProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseFixedIntervalWindowsProcessing</span></code></a>([...])</p></td>
+<td><p>Base class for fixed interval windows processing.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.paradigms.base.BaseParadigm.html#moabb.paradigms.base.BaseParadigm" title="moabb.paradigms.base.BaseParadigm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseParadigm</span></code></a>(filters[, events, tmin, ...])</p></td>
+<td><p>Base class for paradigms.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing" title="moabb.paradigms.base.BaseProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseProcessing</span></code></a>(filters[, tmin, tmax, ...])</p></td>
+<td><p>Base Processing.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
+  
+    <div class="footer-article-item"><!-- Previous / next buttons -->
+<div class="prev-next-area">
+</div></div>
+  
+</div>
+
+                </footer>
+              
+            </div>
+            
+            
+              
+                <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
+
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#motor-imagery-paradigms">Motor Imagery Paradigms</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#p300-paradigms">P300 Paradigms</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ssvep-paradigms">SSVEP Paradigms</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#c-vep-paradigms">c-VEP Paradigms</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#fixed-interval-windows-processings">Fixed Interval Windows Processings</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#base-utils">Base &amp; Utils</a></li>
+</ul>
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+    
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\ No newline at end of file
diff --git a/docs/pipelines.html b/docs/pipelines.html
new file mode 100644
index 00000000..fd4f1ba4
--- /dev/null
+++ b/docs/pipelines.html
@@ -0,0 +1,623 @@
+
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+    <link rel="moabb icon" sizes="180x180" href="_static/moabb_logo.png" type="image/png">
+    <title>Pipelines &#8212; moabb 1.0.0-dev documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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+                gtag('config', 'G-5WJBKDMSTE');
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+    <script>DOCUMENTATION_OPTIONS.pagename = 'pipelines';</script>
+    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
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+document.write(`<div class="bd-header-announcement"></div>`);
+fetch("https://raw.githubusercontent.com/NeuroTechX/moabb/develop/docs/source/_templates/custom-template.html")
+  .then(res => {return res.text();})
+  .then(data => {
+    div = document.querySelector(".bd-header-announcement");
+    div.classList.add(...["bd-header-announcement", "container-fluid"]);
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+  })
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+    console.log("[PST]: Failed to load announcement at: https://raw.githubusercontent.com/NeuroTechX/moabb/develop/docs/source/_templates/custom-template.html");
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+    
+      <div class="navbar-item">
+  
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+  
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+    <img src="_static/moabb_logo.svg" class="logo__image only-light" alt="Logo image"/>
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+        <div class="navbar-item"><nav class="navbar-nav">
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+    Site Navigation
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+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
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+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
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+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
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+                      <a class="nav-link nav-internal" href="overview.html">
+                        Overview
+                      </a>
+                    </li>
+                
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+                      <a class="nav-link nav-internal" href="auto_tutorials/index.html">
+                        Getting Started
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+                    </li>
+                
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+                    </li>
+                
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+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
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+                        API reference
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+                        Release notes
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+        
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+    
+    
+      <div class="sidebar-header-items__center">
+        
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="dataset_summary.html">
+                        Datasets
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="overview.html">
+                        Overview
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_tutorials/index.html">
+                        Getting Started
+                      </a>
+                    </li>
+                
+            <div class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
+                    More
+                </button>
+                <div class="dropdown-menu">
+                    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Gallery
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="main_concepts.html">
+                        How does Moabb work?
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
+                        API reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        Release notes
+                      </a>
+                    </li>
+                
+                </div>
+            </div>
+            
+  </ul>
+</nav></div>
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+        
+      </div>
+    
+  </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
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+
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+<nav aria-label="Breadcrumbs">
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+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    <li class="breadcrumb-item active" aria-current="page">Pipelines</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section id="module-moabb.pipelines">
+<span id="pipelines"></span><h1>Pipelines<a class="headerlink" href="#module-moabb.pipelines" title="Permalink to this heading">#</a></h1>
+<p>Pipeline defines all steps required by an algorithm to obtain predictions.</p>
+<p>Pipelines are typically a chain of sklearn compatible transformers and
+end with a sklearn compatible estimator.</p>
+<section id="id1">
+<h2>Pipelines<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.LogVariance.html#moabb.pipelines.features.LogVariance" title="moabb.pipelines.features.LogVariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.LogVariance</span></code></a>()</p></td>
+<td><p>LogVariance transformer.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.FM.html#moabb.pipelines.features.FM" title="moabb.pipelines.features.FM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.FM</span></code></a>([freq])</p></td>
+<td><p>Transformer to scale sampling frequency.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.ExtendedSSVEPSignal.html#moabb.pipelines.features.ExtendedSSVEPSignal" title="moabb.pipelines.features.ExtendedSSVEPSignal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.ExtendedSSVEPSignal</span></code></a>()</p></td>
+<td><p>Prepare FilterBank SSVEP EEG signal for estimating extended covariances.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.AugmentedDataset.html#moabb.pipelines.features.AugmentedDataset" title="moabb.pipelines.features.AugmentedDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.AugmentedDataset</span></code></a>([order, lag])</p></td>
+<td><p>Dataset augmentation methods in a higher dimensional space.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.features.StandardScaler_Epoch.html#moabb.pipelines.features.StandardScaler_Epoch" title="moabb.pipelines.features.StandardScaler_Epoch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">features.StandardScaler_Epoch</span></code></a>()</p></td>
+<td><p>Function to standardize the X raw data for the DeepLearning Method.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.csp.TRCSP.html#moabb.pipelines.csp.TRCSP" title="moabb.pipelines.csp.TRCSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">csp.TRCSP</span></code></a>([nfilter, metric, log, alpha])</p></td>
+<td><p>Weighted Tikhonov-regularized CSP as described in Lotte and Guan 2011.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_CCA.html#moabb.pipelines.classification.SSVEP_CCA" title="moabb.pipelines.classification.SSVEP_CCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification.SSVEP_CCA</span></code></a>(interval, freqs[, ...])</p></td>
+<td><p>Classifier based on Canonical Correlation Analysis for SSVEP.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_TRCA.html#moabb.pipelines.classification.SSVEP_TRCA" title="moabb.pipelines.classification.SSVEP_TRCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification.SSVEP_TRCA</span></code></a>(interval, freqs[, ...])</p></td>
+<td><p>Classifier based on the Task-Related Component Analysis method <a href="#id11"><span class="problematic" id="id2">[1]_</span></a> for SSVEP.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.classification.SSVEP_MsetCCA.html#moabb.pipelines.classification.SSVEP_MsetCCA" title="moabb.pipelines.classification.SSVEP_MsetCCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification.SSVEP_MsetCCA</span></code></a>(freqs[, ...])</p></td>
+<td><p>Classifier based on MsetCCA for SSVEP.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#moabb.pipelines.deep_learning.KerasDeepConvNet" title="moabb.pipelines.deep_learning.KerasDeepConvNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasDeepConvNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the Deep Convolutional Network as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasDeepConvNet.html#r679315cfbef6-1" id="id3"><span>[R679315cfbef6-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#moabb.pipelines.deep_learning.KerasEEGITNet" title="moabb.pipelines.deep_learning.KerasEEGITNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGITNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the EEGITNet as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGITNet.html#rf5b2ee1af1ae-1" id="id4"><span>[Rf5b2ee1af1ae-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#moabb.pipelines.deep_learning.KerasEEGNet_8_2" title="moabb.pipelines.deep_learning.KerasEEGNet_8_2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGNet_8_2</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the EEGNet as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNet_8_2.html#rd83becb56589-1" id="id5"><span>[Rd83becb56589-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#moabb.pipelines.deep_learning.KerasEEGNeX" title="moabb.pipelines.deep_learning.KerasEEGNeX"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGNeX</span></code></a>(loss[, optimizer, ...])</p></td>
+<td><p>Keras implementation of the EEGNex as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGNeX.html#r643fa75c3283-1" id="id6"><span>[R643fa75c3283-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#moabb.pipelines.deep_learning.KerasEEGTCNet" title="moabb.pipelines.deep_learning.KerasEEGTCNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasEEGTCNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the EEGTCNet as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasEEGTCNet.html#r89b58824c471-1" id="id7"><span>[R89b58824c471-1]</span></a>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#moabb.pipelines.deep_learning.KerasShallowConvNet" title="moabb.pipelines.deep_learning.KerasShallowConvNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deep_learning.KerasShallowConvNet</span></code></a>(loss[, ...])</p></td>
+<td><p>Keras implementation of the Shallow Convolutional Network as described in <a class="reference internal" href="generated/moabb.pipelines.deep_learning.KerasShallowConvNet.html#r2ccacb732305-1" id="id8"><span>[R2ccacb732305-1]</span></a>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="base-utils">
+<h2>Base &amp; Utils<a class="headerlink" href="#base-utils" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils.create_pipeline_from_config.html#moabb.pipelines.utils.create_pipeline_from_config" title="moabb.pipelines.utils.create_pipeline_from_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.create_pipeline_from_config</span></code></a>(config)</p></td>
+<td><p>Create a pipeline from a config file.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils.FilterBank.html#moabb.pipelines.utils.FilterBank" title="moabb.pipelines.utils.FilterBank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.FilterBank</span></code></a>(estimator[, flatten])</p></td>
+<td><p>Apply a given identical pipeline over a bank of filter.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet.html#moabb.pipelines.utils_deep_model.EEGNet" title="moabb.pipelines.utils_deep_model.EEGNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_deep_model.EEGNet</span></code></a>(data, input_layer[, ...])</p></td>
+<td><p>EEGNet block implementation as described in <a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet.html#r820c2366bc63-1" id="id9"><span>[R820c2366bc63-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.EEGNet_TC.html#moabb.pipelines.utils_deep_model.EEGNet_TC" title="moabb.pipelines.utils_deep_model.EEGNet_TC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_deep_model.EEGNet_TC</span></code></a>(self, input_layer)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.TCN_block.html#moabb.pipelines.utils_deep_model.TCN_block" title="moabb.pipelines.utils_deep_model.TCN_block"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_deep_model.TCN_block</span></code></a>(input_layer, ...)</p></td>
+<td><p>Temporal Convolutional Network (TCN), TCN_block from <a class="reference internal" href="generated/moabb.pipelines.utils_deep_model.TCN_block.html#r2eea69aed7b6-1" id="id10"><span>[R2eea69aed7b6-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader.html#moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader" title="moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_pytorch.BraindecodeDatasetLoader</span></code></a>([...])</p></td>
+<td><p>Class to Load the data from MOABB in a format compatible with braindecode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.pipelines.utils_pytorch.InputShapeSetterEEG.html#moabb.pipelines.utils_pytorch.InputShapeSetterEEG" title="moabb.pipelines.utils_pytorch.InputShapeSetterEEG"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils_pytorch.InputShapeSetterEEG</span></code></a>([...])</p></td>
+<td><p>Sets the input dimension of the PyTorch module to the input dimension of the training data.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
+                <footer class="bd-footer-article">
+                  
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+document.write(`<div class="bd-header-announcement"></div>`);
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+    div = document.querySelector(".bd-header-announcement");
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+                      <a class="nav-link nav-internal" href="main_concepts.html">
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SSVEP", "Computation times", "Tutorial 4: Creating a dataset class", "Getting Started", "Tutorial 5: Creating a dataset class", "Tutorial 0: Getting Started", "Computation times", "Tutorial 1: Simple Motor Imagery", "Tutorial 2: Using multiple datasets", "Tutorial 3: Benchmarking multiple pipelines", "Citing MOABB and related publications", "Data Summary", "Datasets", "Evaluations", "moabb.analysis.meta_analysis.collapse_session_scores", "moabb.analysis.meta_analysis.combine_effects", "moabb.analysis.meta_analysis.combine_pvalues", "moabb.analysis.meta_analysis.compute_dataset_statistics", "moabb.analysis.meta_analysis.find_significant_differences", "moabb.analysis.plotting.meta_analysis_plot", "moabb.analysis.plotting.paired_plot", "moabb.analysis.plotting.score_plot", "moabb.analysis.plotting.summary_plot", "moabb.benchmark", "moabb.datasets.AlexMI", "moabb.datasets.BI2012", "moabb.datasets.BI2013a", "moabb.datasets.BI2014a", "moabb.datasets.BI2014b", "moabb.datasets.BI2015a", "moabb.datasets.BI2015b", "moabb.datasets.BNCI2014_001", "moabb.datasets.BNCI2014_002", "moabb.datasets.BNCI2014_004", "moabb.datasets.BNCI2014_008", "moabb.datasets.BNCI2014_009", "moabb.datasets.BNCI2015_001", "moabb.datasets.BNCI2015_003", "moabb.datasets.BNCI2015_004", "moabb.datasets.CastillosBurstVEP100", "moabb.datasets.CastillosBurstVEP40", "moabb.datasets.CastillosCVEP100", "moabb.datasets.CastillosCVEP40", "moabb.datasets.Cattan2019_PHMD", "moabb.datasets.Cattan2019_VR", "moabb.datasets.Cho2017", "moabb.datasets.DemonsP300", "moabb.datasets.EPFLP300", "moabb.datasets.GrosseWentrup2009", "moabb.datasets.Huebner2017", "moabb.datasets.Huebner2018", "moabb.datasets.Kalunga2016", "moabb.datasets.Lee2019_ERP", "moabb.datasets.Lee2019_MI", "moabb.datasets.Lee2019_SSVEP", "moabb.datasets.MAMEM1", "moabb.datasets.MAMEM2", "moabb.datasets.MAMEM3", "moabb.datasets.Nakanishi2015", "moabb.datasets.Ofner2017", "moabb.datasets.PhysionetMI", "moabb.datasets.Schirrmeister2017", "moabb.datasets.Shin2017A", "moabb.datasets.Shin2017B", "moabb.datasets.Sosulski2019", "moabb.datasets.Thielen2015", "moabb.datasets.Thielen2021", "moabb.datasets.Wang2016", "moabb.datasets.Weibo2014", "moabb.datasets.Zhou2016", "moabb.datasets.base.BaseDataset", "moabb.datasets.base.CacheConfig", "moabb.datasets.compound_dataset.BI2014a_Il", "moabb.datasets.compound_dataset.BI2014b_Il", "moabb.datasets.compound_dataset.BI2015a_Il", "moabb.datasets.compound_dataset.BI2015b_Il", "moabb.datasets.compound_dataset.BI_Il", "moabb.datasets.compound_dataset.Cattan2019_VR_Il", "moabb.datasets.download.data_dl", "moabb.datasets.download.data_path", "moabb.datasets.download.fs_get_file_hash", "moabb.datasets.download.fs_get_file_id", "moabb.datasets.download.fs_get_file_list", "moabb.datasets.download.fs_get_file_name", "moabb.datasets.download.fs_issue_request", "moabb.datasets.fake.FakeDataset", "moabb.datasets.fake.FakeVirtualRealityDataset", "moabb.datasets.utils.dataset_search", "moabb.datasets.utils.find_intersecting_channels", "moabb.evaluations.CrossSessionEvaluation", "moabb.evaluations.CrossSubjectEvaluation", "moabb.evaluations.WithinSessionEvaluation", "moabb.evaluations.base.BaseEvaluation", "moabb.make_process_pipelines", "moabb.paradigms.BaseFixedIntervalWindowsProcessing", "moabb.paradigms.CVEP", "moabb.paradigms.FilterBankCVEP", "moabb.paradigms.FilterBankFixedIntervalWindowsProcessing", "moabb.paradigms.FilterBankLeftRightImagery", "moabb.paradigms.FilterBankMotorImagery", "moabb.paradigms.FilterBankSSVEP", "moabb.paradigms.FixedIntervalWindowsProcessing", "moabb.paradigms.LeftRightImagery", "moabb.paradigms.MotorImagery", "moabb.paradigms.P300", "moabb.paradigms.SSVEP", "moabb.paradigms.SinglePass", "moabb.paradigms.base.BaseParadigm", "moabb.paradigms.base.BaseProcessing", "moabb.paradigms.motor_imagery.BaseMotorImagery", "moabb.paradigms.motor_imagery.FilterBank", "moabb.paradigms.motor_imagery.SinglePass", "moabb.paradigms.p300.BaseP300", "moabb.paradigms.ssvep.BaseSSVEP", "moabb.pipelines.classification.SSVEP_CCA", "moabb.pipelines.classification.SSVEP_MsetCCA", "moabb.pipelines.classification.SSVEP_TRCA", "moabb.pipelines.csp.TRCSP", "moabb.pipelines.deep_learning.KerasDeepConvNet", "moabb.pipelines.deep_learning.KerasEEGITNet", "moabb.pipelines.deep_learning.KerasEEGNeX", "moabb.pipelines.deep_learning.KerasEEGNet_8_2", "moabb.pipelines.deep_learning.KerasEEGTCNet", "moabb.pipelines.deep_learning.KerasShallowConvNet", "moabb.pipelines.features.AugmentedDataset", "moabb.pipelines.features.ExtendedSSVEPSignal", "moabb.pipelines.features.FM", "moabb.pipelines.features.LogVariance", "moabb.pipelines.features.StandardScaler_Epoch", "moabb.pipelines.utils.FilterBank", "moabb.pipelines.utils.create_pipeline_from_config", "moabb.pipelines.utils_deep_model.EEGNet", "moabb.pipelines.utils_deep_model.EEGNet_TC", "moabb.pipelines.utils_deep_model.TCN_block", "moabb.pipelines.utils_pytorch.BraindecodeDatasetLoader", 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27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 176], "exampl": [1, 5, 6, 7, 8, 9, 11, 12, 14, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 92, 108, 132, 133, 145, 146, 149, 166, 176, 178, 180, 182, 187], "current": [1, 11, 25, 26, 28, 29, 30, 31, 48, 86, 89, 101, 107, 161, 176], "pip": [177, 178, 179], "fail": 187, "21": [25, 36, 38, 43, 44, 48, 81, 101, 187], "e": [1, 3, 22, 26, 27, 29, 32, 33, 34, 35, 44, 47, 48, 63, 64, 65, 70, 71, 74, 75, 84, 85, 87, 88, 89, 90, 91, 93, 94, 96, 97, 99, 100, 101, 103, 104, 107, 129, 132, 133, 135, 136, 137, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 172, 174, 176, 179, 181, 184], "g": [27, 34, 44, 45, 46, 48, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 80, 81, 87, 96, 97, 100, 101, 132, 133, 135, 136, 137, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 152, 153, 155, 156, 157, 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165, 166, 168, 171, 172, 174, 181, 184, 187], "store": [3, 7, 12, 22, 31, 37, 39, 44, 48, 49, 60, 61, 62, 63, 64, 65, 66, 67, 71, 80, 81, 82, 83, 84, 85, 88, 95, 96, 97, 98, 101, 104, 105, 106, 107, 108, 115, 116, 122, 126, 127, 128, 129, 181, 187], "thei": [3, 5, 9, 29, 33, 35, 39, 42, 44, 48, 49, 60, 69, 70, 88, 99, 100, 102, 108, 151, 152, 181, 182], "been": [3, 7, 11, 33, 39, 48, 49, 61, 89, 92, 93, 94, 105, 155, 156, 157, 158, 159, 160, 168, 179, 181], "convert": [3, 16, 24, 29, 33, 34, 38, 48, 49, 61, 72, 162, 167, 174, 181, 184, 187], "mne": [3, 4, 5, 8, 9, 10, 11, 16, 18, 19, 22, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 39, 44, 45, 46, 48, 49, 57, 59, 61, 62, 63, 64, 65, 66, 67, 72, 80, 81, 82, 83, 84, 85, 88, 95, 96, 97, 98, 101, 104, 105, 106, 107, 108, 115, 116, 122, 126, 127, 128, 129, 131, 132, 133, 134, 137, 138, 140, 141, 142, 143, 145, 146, 148, 149, 150, 173, 174, 181, 182, 187], "raw": [3, 22, 25, 28, 33, 34, 35, 39, 44, 48, 49, 61, 81, 107, 108, 123, 126, 127, 128, 129, 131, 134, 138, 145, 165, 181, 184, 187], "object": [3, 5, 12, 16, 21, 22, 25, 33, 38, 42, 44, 46, 48, 49, 60, 68, 71, 72, 85, 107, 108, 136, 139, 140, 143, 148, 151, 152, 153, 155, 156, 157, 158, 159, 160, 181, 187], "There": [3, 8, 9, 48, 49, 61, 73, 88, 94, 177, 181], "pool": [3, 48, 49, 181], "record": [3, 33, 34, 39, 42, 45, 48, 49, 50, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 131, 134, 138, 181, 187], "session": [3, 4, 5, 7, 8, 9, 10, 13, 15, 16, 17, 20, 22, 32, 34, 35, 38, 39, 41, 42, 44, 48, 49, 50, 60, 61, 63, 64, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 122, 124, 126, 127, 128, 129, 139, 140, 141, 142, 143, 145, 148, 174, 175, 181, 182, 183, 187], "subject": [3, 4, 5, 6, 7, 8, 9, 12, 14, 16, 18, 19, 22, 23, 24, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 42, 44, 45, 46, 48, 49, 50, 52, 53, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 122, 123, 124, 126, 127, 128, 129, 137, 142, 145, 174, 181, 183, 187], "see": [3, 6, 22, 33, 34, 35, 42, 48, 49, 61, 62, 63, 64, 65, 66, 67, 70, 74, 80, 81, 82, 83, 84, 85, 88, 89, 90, 91, 95, 96, 97, 98, 101, 103, 104, 105, 106, 107, 108, 115, 116, 122, 131, 132, 133, 134, 137, 138, 140, 142, 143, 145, 146, 148, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 178, 179, 181], "electrod": [3, 4, 10, 16, 39, 45, 48, 49, 57, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 125, 128, 129, 139, 143, 148, 153], "number": [3, 9, 14, 16, 17, 18, 19, 24, 25, 28, 29, 33, 34, 35, 39, 41, 44, 45, 47, 48, 49, 52, 53, 60, 61, 62, 63, 64, 65, 66, 67, 72, 80, 81, 82, 83, 84, 85, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 104, 105, 106, 107, 122, 123, 126, 127, 128, 129, 132, 133, 136, 137, 140, 142, 145, 150, 151, 152, 153, 176, 182, 187], "trial": [3, 16, 17, 34, 38, 39, 42, 44, 45, 48, 49, 50, 61, 63, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 153, 181, 184, 187], "etc": [3, 44, 45, 48, 49, 50, 63, 85, 101, 181], "defin": [3, 6, 7, 9, 14, 18, 19, 21, 22, 24, 25, 28, 29, 30, 32, 34, 36, 39, 41, 42, 44, 50, 60, 86, 87, 94, 107, 122, 129, 131, 132, 133, 135, 136, 137, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 172, 181, 184, 185, 187], "how": [3, 6, 7, 8, 9, 11, 12, 14, 16, 17, 18, 19, 21, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 45, 48, 50, 58, 60, 74, 85, 86, 106, 139, 143, 148, 151, 152, 153, 155, 156, 157, 158, 159, 160, 174, 181, 182, 183, 184], "gener": [3, 5, 6, 7, 8, 11, 12, 14, 16, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 50, 56, 57, 63, 97, 99, 100, 104, 107, 122, 126, 127, 128, 129, 151, 174, 181, 183], "auc": [3, 5, 6, 7, 9, 14, 18, 19, 24, 30, 31, 32, 34, 36, 37, 50, 63, 109, 110, 111, 112, 113, 114, 132, 133, 135, 136, 137, 139, 140, 141, 142, 144, 146, 149, 181], "f": [3, 6, 7, 8, 9, 11, 14, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 42, 50, 70, 71, 72, 74, 135, 136, 139, 140, 141, 144, 146, 149, 181], "accuraci": [3, 6, 18, 19, 21, 29, 32, 34, 37, 50, 63, 82, 132, 133, 135, 136, 137, 139, 140, 141, 142, 144, 146, 149, 150, 181, 183], "within": [3, 4, 8, 9, 10, 13, 15, 16, 17, 20, 23, 26, 27, 29, 34, 35, 38, 42, 50, 60, 68, 70, 72, 87, 94, 98, 102, 103, 104, 107, 126, 127, 128, 129, 136, 139, 140, 141, 142, 143, 148, 172, 174, 181, 183], "across": [3, 5, 7, 14, 18, 19, 25, 30, 42, 50, 55, 56, 126, 181, 183, 187], "transfer": [3, 50, 101, 181], "decod": [3, 4, 5, 7, 8, 9, 16, 18, 19, 26, 27, 30, 31, 33, 34, 36, 44, 45, 46, 86, 87, 96, 98, 132, 133, 155, 157, 160, 181, 184], "function": [3, 8, 12, 14, 18, 19, 22, 24, 25, 26, 27, 28, 32, 34, 36, 39, 42, 52, 53, 88, 101, 107, 115, 116, 129, 145, 150, 152, 165, 167, 172, 181, 182, 184, 187], "us": [3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 53, 54, 55, 62, 63, 65, 66, 67, 71, 75, 76, 77, 78, 79, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 108, 116, 122, 132, 133, 144, 145, 146, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 166, 168, 172, 177, 178, 179, 181, 182, 183, 184, 187], "two": [3, 5, 6, 8, 9, 18, 19, 24, 26, 27, 30, 31, 32, 33, 34, 35, 36, 41, 42, 45, 46, 56, 63, 68, 70, 71, 72, 75, 82, 84, 85, 89, 96, 97, 98, 99, 100, 101, 102, 104, 106, 126, 145, 154, 180, 181, 184], "class": [3, 7, 14, 16, 17, 18, 19, 24, 29, 30, 32, 34, 36, 37, 40, 43, 44, 45, 48, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 122, 123, 126, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 171, 181, 182, 184, 187], "multi": [3, 24, 34, 65, 67, 151, 181, 184], "continu": [3, 34, 42, 63, 76, 77, 78, 79, 98, 99, 104, 178, 181, 184], "similarli": [3, 34, 102, 181, 184], "necessari": [3, 9, 18, 30, 32, 33, 34, 36, 44, 129, 153, 181, 184], "v": [1, 3, 9, 18, 26, 27, 29, 34, 47, 48, 58, 64, 65, 66, 67, 83, 92, 158, 168, 176, 181, 184], "erd": [3, 34, 73, 181, 184], "predict": [3, 24, 71, 72, 83, 132, 133, 151, 152, 153, 181, 185], "typic": [3, 89, 181, 185], "chain": [3, 181, 185], "sklearn": [3, 5, 6, 7, 8, 9, 14, 18, 19, 21, 24, 25, 28, 29, 30, 31, 32, 36, 37, 39, 41, 42, 44, 45, 46, 129, 135, 136, 139, 140, 141, 144, 146, 149, 151, 152, 153, 155, 156, 157, 158, 159, 160, 166, 167, 172, 181, 185, 187], "compat": [3, 34, 35, 61, 62, 63, 64, 65, 66, 67, 80, 81, 82, 83, 84, 85, 88, 95, 96, 97, 98, 101, 104, 105, 106, 107, 115, 116, 122, 126, 127, 128, 129, 131, 135, 136, 139, 140, 141, 144, 145, 146, 149, 163, 171, 178, 179, 181, 185, 187], "transform": [3, 7, 9, 14, 18, 19, 24, 30, 32, 35, 36, 152, 153, 161, 162, 163, 164, 172, 181, 185, 187], "end": [3, 24, 25, 33, 34, 35, 39, 44, 45, 46, 73, 75, 85, 89, 91, 93, 94, 96, 99, 100, 131, 132, 133, 134, 137, 138, 140, 142, 143, 145, 146, 148, 149, 150, 179, 181, 185], "estim": [3, 5, 6, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 41, 42, 44, 45, 46, 83, 91, 95, 109, 110, 111, 112, 113, 114, 126, 127, 128, 129, 151, 152, 153, 155, 156, 157, 158, 159, 160, 162, 166, 181, 185], "These": [4, 13, 16, 17, 71, 76, 77, 78, 79, 89, 93, 94, 102, 103, 107, 151, 152, 153], "show": [4, 5, 7, 8, 9, 14, 16, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 44, 45, 46, 56, 60, 68, 84, 86, 96, 98, 99, 100, 106, 139, 174, 187], "variou": [4, 13, 16, 101, 182], "topic": [4, 16, 182], "scikit": [4, 16, 25, 29, 37, 38, 42, 44, 163, 165, 174, 175, 187], "input": [4, 7, 12, 16, 27, 29, 34, 60, 107, 129, 145, 166, 167, 172], "select": [4, 5, 6, 9, 10, 16, 18, 23, 24, 28, 30, 31, 34, 37, 48, 57, 60, 68, 70, 71, 72, 81, 89, 92, 98, 106, 107, 109, 110, 111, 112, 114, 119, 123, 124, 125, 128, 129, 131, 132, 133, 134, 137, 138, 139, 140, 142, 143, 145, 146, 148, 149, 150, 153, 172, 187], "resampl": [4, 10, 14, 16, 19, 33, 34, 35, 36, 57, 68, 106, 107, 125, 128, 129, 131, 132, 133, 134, 137, 138, 139, 140, 142, 143, 144, 145, 146, 148, 149, 150], "filterbank": [4, 10, 16, 32, 68, 107, 126, 129, 133, 135, 139, 143, 148, 153, 162, 174], "approach": [4, 16, 25, 83, 153, 162], "motor": [4, 16, 17, 20, 22, 26, 31, 32, 34, 38, 40, 43, 61, 68, 69, 70, 73, 75, 82, 85, 96, 97, 98, 99, 100, 105, 106, 107, 124, 126, 128, 129, 131, 135, 136, 139, 140, 143, 145, 146, 148, 149, 156, 159, 170, 174, 175, 181, 187], "imageri": [4, 9, 16, 17, 20, 22, 25, 26, 31, 32, 34, 38, 39, 40, 42, 43, 61, 68, 69, 70, 73, 75, 82, 85, 90, 96, 97, 99, 105, 106, 107, 122, 124, 126, 128, 129, 131, 135, 136, 139, 140, 143, 145, 146, 148, 149, 156, 159, 170, 174, 175, 181, 187], "statist": [4, 7, 10, 16, 42, 50, 51, 52, 53, 54, 56, 57, 58, 59, 68, 101, 107, 126, 129, 139, 143, 148, 174], "meta": [4, 7, 9, 16, 31, 37, 39, 44, 52, 53, 54, 56, 151, 152, 153, 155, 156, 157, 158, 159, 160, 187], "analysi": [4, 7, 8, 10, 12, 14, 16, 18, 19, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 37, 38, 44, 46, 60, 68, 71, 80, 85, 92, 95, 107, 126, 129, 139, 143, 148, 151, 152, 153, 174, 181, 182, 183, 187], "gridsearch": [4, 10, 16, 28, 68, 107, 128, 129, 140, 143, 148, 187], "csp": [4, 8, 9, 10, 12, 16, 18, 21, 25, 28, 30, 31, 44, 45, 46, 68, 107, 126, 129, 135, 139, 143, 148, 166, 174, 187], "versu": [4, 9, 10, 16, 30, 63, 65, 67, 68, 73, 83, 107, 126, 129, 135, 139, 143, 148, 174], "epoch": [4, 6, 8, 9, 10, 14, 16, 18, 19, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 42, 44, 48, 57, 59, 60, 71, 72, 76, 77, 78, 81, 86, 102, 103, 104, 107, 108, 126, 127, 128, 129, 131, 132, 133, 134, 137, 138, 140, 141, 142, 143, 144, 145, 146, 148, 149, 150, 155, 156, 157, 158, 159, 160, 174, 187], "click": [5, 6, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 41, 42, 44, 45, 46], "full": [5, 6, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 41, 42, 44, 45, 46, 60, 62, 64, 65, 66, 67, 80, 81, 102, 103, 179], "comparison": [5, 7, 9, 46, 87, 95, 181, 183], "2a": [5, 18, 30, 44], "bci": [5, 18, 24, 30, 34, 39, 41, 44, 45, 46, 47, 48, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 76, 77, 78, 79, 81, 82, 83, 85, 88, 89, 90, 91, 92, 95, 97, 101, 102, 103, 104, 152, 161, 183, 187], "competit": [5, 18, 30, 34, 44, 67, 68, 70, 187], "iv": [5, 18, 30, 34, 44, 68, 187], "author": [1, 5, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 41, 42, 44, 45, 46, 47, 80, 153, 155, 156, 157, 158, 159, 160, 168, 176], "alexandr": [1, 5, 18, 22, 30, 34, 47, 62, 63, 176, 187], "gmail": [5, 9, 11, 12, 18, 22, 23, 24, 27, 29, 30, 33, 34, 35, 36, 42], "com": [5, 9, 11, 12, 18, 22, 23, 24, 27, 29, 30, 33, 34, 35, 36, 39, 41, 42, 44, 45, 46, 47, 92, 93, 94, 153, 176, 179], "licens": [5, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 42, 99, 100, 182], "bsd": [5, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 42], "3": [5, 7, 8, 9, 10, 11, 12, 14, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 48, 60, 61, 63, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107, 122, 128, 129, 139, 143, 148, 151, 152, 153, 155, 156, 157, 158, 159, 160, 174, 177, 178], "claus": [5, 7, 8, 9, 11, 12, 14, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 42], "matplotlib": [5, 6, 7, 8, 9, 14, 18, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 44, 45, 46], "pyplot": [5, 6, 7, 8, 9, 12, 14, 18, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 44, 45, 46, 56, 57, 58, 59], "plt": [5, 6, 7, 8, 9, 14, 18, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 44, 45, 46], "panda": [5, 7, 12, 24, 25, 26, 27, 28, 32, 42, 44, 45, 46], "pd": [5, 7, 24, 32, 34, 44, 129, 145], "seaborn": [5, 6, 14, 18, 19, 29, 30, 31, 32, 36, 37, 44, 45, 46], "sn": [5, 6, 14, 18, 19, 29, 30, 31, 32, 36, 37, 44, 45, 46], "discriminant_analysi": 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\ No newline at end of file
diff --git a/docs/utils.html b/docs/utils.html
new file mode 100644
index 00000000..1adada96
--- /dev/null
+++ b/docs/utils.html
@@ -0,0 +1,569 @@
+
+
+<!DOCTYPE html>
+
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+
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+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="paper_results.html">
+                        The largest EEG benchmark
+                      </a>
+                    </li>
+                
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+                        Datasets
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+                
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+                        API reference
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+                      </a>
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+            
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+  </button>
+`);
+</script></div>
+        
+      </div>
+    
+  </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+
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+      
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+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
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+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
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+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    <li class="breadcrumb-item active" aria-current="page">Utils</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section id="module-moabb">
+<span id="utils"></span><h1>Utils<a class="headerlink" href="#module-moabb" title="Permalink to this heading">#</a></h1>
+<section id="benchmark">
+<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark"><code class="xref py py-obj docutils literal notranslate"><span class="pre">benchmark</span></code></a>([pipelines, evaluations, ...])</p></td>
+<td><p>Run benchmarks for selected pipelines and datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id1">
+<h2>Utils<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h2>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.set_log_level.html#moabb.set_log_level" title="moabb.set_log_level"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_log_level</span></code></a>([level])</p></td>
+<td><p>Set log level.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.setup_seed.html#moabb.setup_seed" title="moabb.setup_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setup_seed</span></code></a>(seed)</p></td>
+<td><p>Set the seed for random, numpy, TensorFlow and PyTorch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/moabb.set_download_dir.html#moabb.set_download_dir" title="moabb.set_download_dir"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_download_dir</span></code></a>(path)</p></td>
+<td><p>Set the download directory if required to change from default mne path.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/moabb.make_process_pipelines.html#moabb.make_process_pipelines" title="moabb.make_process_pipelines"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_process_pipelines</span></code></a>(processing, dataset)</p></td>
+<td><p>Shortcut for the method <a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.make_process_pipelines" title="moabb.paradigms.base.BaseProcessing.make_process_pipelines"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseProcessing.make_process_pipelines()</span></code></a></p></td>
+</tr>
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+    
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+                        The largest EEG benchmark
+                      </a>
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+  <section id="what-s-new">
+<span id="whats-new"></span><h1>What’s new<a class="headerlink" href="#what-s-new" title="Permalink to this heading">#</a></h1>
+<ul class="simple">
+<li><p>“Enhancements” for new features</p></li>
+<li><p>“Bugs” for bug fixes</p></li>
+<li><p>“API changes” for backward-incompatible changes</p></li>
+</ul>
+<section id="develop-branch">
+<span id="current"></span><h2>Develop branch<a class="headerlink" href="#develop-branch" title="Permalink to this heading">#</a></h2>
+<section id="enhancements">
+<h3>Enhancements<a class="headerlink" href="#enhancements" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding cache option to the evaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/517">#517</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Option to interpolate channel in paradigms’ <cite>match_all</cite> method (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/480">#480</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a>)</p></li>
+<li><p>Adding leave k-Subjects out evaluations (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/470">#470</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Update Braindecode dependency to 0.8 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/542">#542</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Improve transform function of AugmentedDataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/541">#541</a> by <a class="reference external" href="https://github.com/qbarthelemy">Quentin Barthelemy</a>)</p></li>
+<li><p>Adding new paper results website (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/556">#556</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Move cVEP common functions to <code class="xref py py-mod docutils literal notranslate"><span class="pre">moabb.datasets.utils</span></code> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/564">#564</a> <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/557">#557</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Normalize c-VEP description tables (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/562">#562</a> <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/566">#566</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Update citation in README (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/573">#573</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+</ul>
+</section>
+<section id="bugs">
+<h3>Bugs<a class="headerlink" href="#bugs" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Fix TRCA implementation for different stimulation freqs and for signal filtering (:gh:522 by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix saving to BIDS runs with a description string in their name (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/530">#530</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix import of keras BatchNormalization for TF 2.13 and higher (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/544">#544</a> by <a class="reference external" href="https://github.com/brianjohannes">Brian Irvine</a>)</p></li>
+<li><p>Fix the doc summary tables of <a class="reference internal" href="generated/moabb.datasets.Lee2019_SSVEP.html#moabb.datasets.Lee2019_SSVEP" title="moabb.datasets.Lee2019_SSVEP"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.Lee2019_SSVEP</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/548">#548</a> <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/547">#547</a> <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/546">#546</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix the doc summary for Castillos2023 dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/561">#561</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fix format string receiving incorrect number of args in bids interface (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/563">#563</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix number of sessions in doc of <a class="reference internal" href="generated/moabb.datasets.Sosulski2019.html#moabb.datasets.Sosulski2019" title="moabb.datasets.Sosulski2019"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.Sosulski2019</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/565">#565</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix <cite>code</cite> column of <a class="reference internal" href="generated/moabb.datasets.CastillosCVEP100.html#moabb.datasets.CastillosCVEP100" title="moabb.datasets.CastillosCVEP100"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.CastillosCVEP100</span></code></a> and <a class="reference internal" href="generated/moabb.datasets.CastillosCVEP100.html#moabb.datasets.CastillosCVEP100" title="moabb.datasets.CastillosCVEP100"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.datasets.CastillosCVEP100</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/567">#567</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+</ul>
+</section>
+<section id="api-changes">
+<h3>API changes<a class="headerlink" href="#api-changes" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+</section>
+<section id="version-1-0-0-stable-pypi">
+<h2>Version - 1.0.0  (Stable - PyPi)<a class="headerlink" href="#version-1-0-0-stable-pypi" title="Permalink to this heading">#</a></h2>
+<section id="id1">
+<h3>Enhancements<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding extra thank you section in the documentation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/390">#390</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding new script to get the meta information of the datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/389">#389</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixing the dataset description based on the meta information (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/389">#389</a> and <cite>398</cite> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Adding second deployment of the documentation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/374">#374</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding Parallel evaluation for <a class="reference internal" href="generated/moabb.evaluations.WithinSessionEvaluation.html#moabb.evaluations.WithinSessionEvaluation" title="moabb.evaluations.WithinSessionEvaluation"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.evaluations.WithinSessionEvaluation()</span></code></a> , <a class="reference internal" href="generated/moabb.evaluations.CrossSessionEvaluation.html#moabb.evaluations.CrossSessionEvaluation" title="moabb.evaluations.CrossSessionEvaluation"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.evaluations.CrossSessionEvaluation()</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/364">#364</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add example with VirtualReality BrainInvaders dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/393">#393</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a> and <a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a>)</p></li>
+<li><p>Adding saving option for the models (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/401">#401</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Adding example to load different type of models (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/401">#401</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Add resting state paradigm with dataset and example (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/400">#400</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a> and <a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a>)</p></li>
+<li><p>Speeding the augmentation method by 400% with NumPy vectorization  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/419">#419</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add possibility to convert datasets to BIDS, plus <a class="reference external" href="examples/example_bids_conversion.py">example</a> (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a>, PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/391">#391</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Allow caching intermediate processing steps on disk, plus <a class="reference external" href="examples/example_disk_cache.py">example</a> (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a>, issue <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/385">#385</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Restructure the paradigms and datasets to move all preprocessing steps to <code class="xref py py-mod docutils literal notranslate"><span class="pre">moabb.datasets.preprocessing</span></code> and as sklearn pipelines (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add <a class="reference internal" href="generated/moabb.paradigms.FixedIntervalWindowsProcessing.html#moabb.paradigms.FixedIntervalWindowsProcessing" title="moabb.paradigms.FixedIntervalWindowsProcessing"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.FixedIntervalWindowsProcessing()</span></code></a> and <a class="reference internal" href="generated/moabb.paradigms.FilterBankFixedIntervalWindowsProcessing.html#moabb.paradigms.FilterBankFixedIntervalWindowsProcessing" title="moabb.paradigms.FilterBankFixedIntervalWindowsProcessing"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.FilterBankFixedIntervalWindowsProcessing()</span></code></a>, plus <a class="reference external" href="examples/example_fixed_interval_windows.py">example</a> (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a>, issue <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/424">#424</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Define <a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing" title="moabb.paradigms.base.BaseProcessing"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseProcessing()</span></code></a>, common parent to <a class="reference internal" href="generated/moabb.paradigms.base.BaseParadigm.html#moabb.paradigms.base.BaseParadigm" title="moabb.paradigms.base.BaseParadigm"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseParadigm()</span></code></a> and <a class="reference internal" href="generated/moabb.paradigms.BaseFixedIntervalWindowsProcessing.html#moabb.paradigms.BaseFixedIntervalWindowsProcessing" title="moabb.paradigms.BaseFixedIntervalWindowsProcessing"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.BaseFixedIntervalWindowsProcessing()</span></code></a> (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Allow passing a fixed processing pipeline to <a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing.get_data" title="moabb.paradigms.base.BaseProcessing.get_data"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseProcessing.get_data()</span></code></a> and cache its result on disk (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a>, issue <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/367">#367</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Update <a class="reference internal" href="generated/moabb.datasets.fake.FakeDataset.html#moabb.datasets.fake.FakeDataset" title="moabb.datasets.fake.FakeDataset"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.fake.FakeDataset()</span></code></a>’s code to be unique for each parameter combination (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Systematically set the annotations when loading data, eventually using the stim channel (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Allow <a class="reference internal" href="generated/moabb.datasets.utils.dataset_search.html#moabb.datasets.utils.dataset_search" title="moabb.datasets.utils.dataset_search"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.utils.dataset_search()</span></code></a> to search across paradigms <code class="docutils literal notranslate"><span class="pre">paradigm=None</span></code> (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Improving the review processing with more pre-commit bots (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/435">#435</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add methods <code class="docutils literal notranslate"><span class="pre">make_processing_pipelines</span></code> and <code class="docutils literal notranslate"><span class="pre">make_labels_pipeline</span></code> to <a class="reference internal" href="generated/moabb.paradigms.base.BaseProcessing.html#moabb.paradigms.base.BaseProcessing" title="moabb.paradigms.base.BaseProcessing"><code class="xref py py-class docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseProcessing</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/447">#447</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Pipelines’ digests are now computed from the whole processing+classification pipeline (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/447">#447</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Update all dataset codes to remove white spaces and underscores (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/448">#448</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.utils.depreciated_alias()</span></code> decorator (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/455">#455</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Rename many dataset class names to standardize and deprecate old names (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/455">#455</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Change many dataset codes to match the class names (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/455">#455</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add <code class="xref py py-obj docutils literal notranslate"><span class="pre">moabb.datasets.compound_dataset.utils.compound_dataset_list</span></code>  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/455">#455</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add c-VEP paradigm and Thielen2021 c-VEP dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/463">#463</a> by <a class="reference external" href="https://github.com/thijor">Jordy Thielen</a>)</p></li>
+<li><p>Add option to plot scores vertically. (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/417">#417</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Change naming scheme for runs and sessions to align to BIDS standard (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/471">#471</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Increase the python version to 3.11 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/470">#470</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add match_all method in paradigm to support CompoundDataset evaluation with MNE epochs (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/473">#473</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a>)</p></li>
+<li><p>Automate setting of event_id in compound dataset and add <cite>data_origin</cite> information to the data (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/475">#475</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a>)</p></li>
+<li><p>Add possibility of not saving the model (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/489">#489</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Add CVEP and BurstVEP dataset from Castillos from Toulouse lab (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/531">#531</a> by <a class="reference external" href="https://github.com/swetbear">Sebastien Velut</a>)</p></li>
+<li><p>Add c-VEP dataset from Thielen et al. 2015 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/557">#557</a> by <a class="reference external" href="https://github.com/thijor">Jordy Thielen</a>)</p></li>
+</ul>
+</section>
+<section id="id4">
+<h3>Bugs<a class="headerlink" href="#id4" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Restore 3 subject from Cho2017 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/392">#392</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Correct downloading with VirtualReality BrainInvaders dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/393">#393</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a>)</p></li>
+<li><p>Rename event <cite>subtraction</cite> in <a class="reference internal" href="generated/moabb.datasets.Shin2017B.html#moabb.datasets.Shin2017B" title="moabb.datasets.Shin2017B"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.Shin2017B()</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/397">#397</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Save parameters of <a class="reference internal" href="generated/moabb.datasets.PhysionetMI.html#moabb.datasets.PhysionetMI" title="moabb.datasets.PhysionetMI"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.PhysionetMI()</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/403">#403</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fixing issue with parallel evaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/401">#401</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Fixing SSLError from BCI competition IV (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/404">#404</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixing <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.bnci.MNEBNCI.data_path()</span></code> that returned the data itself instead of paths (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/412">#412</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Adding <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.fake()</span></code> in the init file to use in braindecode object (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/414">#414</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixing the parallel download issue when the dataset have the same directory (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/421">#421</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Fixing fixes the problem with the annotation loading for the P300 datasets Sosulski2019, Huebner2017 and Huebner2018 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/396">#396</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Removing the print in the dataset list (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/423">#423</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixing bug in <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.pipeline.utils_pytorch.BraindecodeDatasetLoader()</span></code> where incorrect y was used in transform calls (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/426">#426</a> by <a class="reference external" href="https://github.com/Kaos9001">Gabriel Schwartz</a>)</p></li>
+<li><p>Fixing one test in <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.pipeline.utils_pytorch.BraindecodeDatasetLoader()</span></code> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/426">#426</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fix <a class="reference internal" href="generated/moabb.benchmark.html#moabb.benchmark" title="moabb.benchmark"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.benchmark()</span></code></a> overwriting <code class="docutils literal notranslate"><span class="pre">include_datasets</span></code> list (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix <a class="reference internal" href="generated/moabb.paradigms.base.BaseParadigm.html#moabb.paradigms.base.BaseParadigm" title="moabb.paradigms.base.BaseParadigm"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.base.BaseParadigm()</span></code></a> using attributes before defining them  (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a>, issue <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/425">#425</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.FakeImageryParadigm()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.FakeP300Paradigm()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.paradigms.FakeSSVEPParadigm()</span></code> <code class="docutils literal notranslate"><span class="pre">is_valid</span></code> methods to only accept the correct datasets (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/408">#408</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">dataset_list</span></code> construction, which could be empty due to bad import order (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/449">#449</a> by <a class="reference external" href="https://github.com/tommoral">Thomas Moreau</a>).</p></li>
+<li><p>Fixing dataset downloader from servers with non-http (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/433">#433</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">dataset_list</span></code> to include deprecated datasets (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/464">#464</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixed bug in <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.analysis.results.get_string_rep()</span></code> to handle addresses such as 0x__0A as well (PR <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/468">#468</a> by <a href="#id37"><span class="problematic" id="id38">`Anton Andreev`_</span></a>)</p></li>
+<li><p>Moving the <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.evualation.grid_search()</span></code> to inside the base evaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/487">#487</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Removing joblib Parallel (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/488">#488</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Fix case when events specified via <code class="docutils literal notranslate"><span class="pre">raw.annotations</span></code> but no events (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/491">#491</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix bug in downloading Shin2017A dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/493">#493</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Fix the cropped option in the dataset preprocessing (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/502">#502</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fix bug in <a class="reference internal" href="generated/moabb.datasets.utils.dataset_search.html#moabb.datasets.utils.dataset_search" title="moabb.datasets.utils.dataset_search"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.utils.dataset_search()</span></code></a> with missing cvep paradigm (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/557">#557</a> by <a class="reference external" href="https://github.com/thijor">Jordy Thielen</a>)</p></li>
+<li><p>Fix mistakes in <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.thielen2021()</span></code> considering wrong docs and hardcoded trial stim channel (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/557">#557</a> by <a class="reference external" href="https://github.com/thijor">Jordy Thielen</a>)</p></li>
+</ul>
+</section>
+<section id="id5">
+<h3>API changes<a class="headerlink" href="#id5" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-5-0">
+<h2>Version - 0.5.0<a class="headerlink" href="#version-0-5-0" title="Permalink to this heading">#</a></h2>
+<section id="id6">
+<h3>Enhancements<a class="headerlink" href="#id6" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Speeding the augmentation model (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/365">#365</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add VirtualReality BrainInvaders dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/358">#358</a> by <a class="reference external" href="https://github.com/gcattan">Gregoire Cattan</a>)</p></li>
+<li><p>Switch to python-3.8, update dependencies, fix code link in doc, add <a class="reference external" href="https://app.codecov.io/gh/NeuroTechX/moabb">code coverage</a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/315">#315</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding a comprehensive benchmarking function (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/264">#264</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add meta-information for datasets in documentation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/317">#317</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add GridSearchCV for different evaluation procedure (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/319">#319</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Add new tutorial to benchmark with GridSearchCV (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/323">#323</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Add six deep learning models (Tensorflow), and build a tutorial to show to use the deep learning model (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/326">#326</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add a augmentation model to the pipeline (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/326">#326</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Add BrainDecode example (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/340">#340</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add Google Analytics to the documentation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/335">#335</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add support to Braindecode classifier (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/328">#328</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add CodeCarbon to track emission CO₂ (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/350">#350</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add CodeCarbon example (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/356">#356</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add MsetCCA method for SSVEP classification, parametrise CCA <cite>n_components</cite> in CCA based methods (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/359">#359</a> by <a class="reference external" href="https://github.com/emmanuelkalunga">Emmanuel Kalunga</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Set epochs’ <cite>metadata</cite> field in <cite>get_data</cite> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/371">#371</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add possibility to use transformers to apply fixed pre-processings before evaluations (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/372">#372</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add <cite>seed</cite> parameter to <cite>FakeDataset</cite> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/372">#372</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+</ul>
+</section>
+<section id="id7">
+<h3>Bugs<a class="headerlink" href="#id7" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Fix circular import with braindecode (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/363">#363</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fix bug for MotorImagery when we handle all events (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/327">#327</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Fixing CI to handle with new deep learning dependencies (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/332">#332</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/326">#326</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Correct CI error due to isort (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/330">#330</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Restricting Python &lt;= 3.11 version and adding tensorflow, keras, scikeras, braindecode, skorch and torch, as optional dependence (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/329">#329</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fix numpy variable to handle with the new version of python (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/324">#324</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Correct CI error due to black (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/292">#292</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Preload Schirrmeister2017 raw files (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/290">#290</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Incorrect event assignation for Lee2019 in MNE &gt;= 1.0.0 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/298">#298</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Correct usage of name simplification function in analyze (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/306">#306</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
+<li><p>Fix downloading path issue for Weibo2014 and Zhou2016, numy error in DemonsP300 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/315">#315</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix unzip error for Huebner2017 and Huebner2018 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/318">#318</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix n_classes when events set to None (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/337">#337</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Change n_jobs=-1 to self.n_jobs in GridSearch (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/344">#344</a> by <a class="reference external" href="https://github.com/carraraig">Igor Carrara</a>)</p></li>
+<li><p>Fix dropped epochs issue (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/371">#371</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix redundancy website issue (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/372">#372</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+</ul>
+</section>
+<section id="id8">
+<h3>API changes<a class="headerlink" href="#id8" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-6">
+<h2>Version - 0.4.6<a class="headerlink" href="#version-0-4-6" title="Permalink to this heading">#</a></h2>
+<section id="id9">
+<h3>Enhancements<a class="headerlink" href="#id9" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Add P300 BrainInvaders datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/283">#283</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add explicit warning when lambda function are used to parametrize pipelines (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/278">#278</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+</ul>
+</section>
+<section id="id10">
+<h3>Bugs<a class="headerlink" href="#id10" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Correct default path for ERP visualization (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/279">#279</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Correct documentation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/282">#282</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/284">#284</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-5">
+<h2>Version - 0.4.5<a class="headerlink" href="#version-0-4-5" title="Permalink to this heading">#</a></h2>
+<section id="id11">
+<h3>Enhancements<a class="headerlink" href="#id11" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Progress bars, pooch, tqdm (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/258">#258</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding test and example for set_download_dir (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/249">#249</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
+<li><p>Update to newer version of Schirrmeister2017 dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/265">#265</a> by <a class="reference external" href="https://github.com/robintibor">Robin Schirrmeister</a>)</p></li>
+<li><p>Adding Huebner2017 and Huebner2018 P300 datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/260">#260</a>  by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Adding Sosulski2019 auditory P300 datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/266">#266</a>  by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>New script to visualize ERP on all datasets, as a sanity check (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/261">#261</a>  by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+</ul>
+</section>
+<section id="id12">
+<h3>Bugs<a class="headerlink" href="#id12" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Removing dependency on mne method for PhysionetMI data downloading, renaming runs (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/257">#257</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
+<li><p>Correcting events management in Schirrmeister2017, renaming session and run (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/255">#255</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Switch session and runs in MAMEM1, 2 and 3 to avoid error in WithinSessionEvaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/256">#256</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Correct doctstrings for the documentation, including Lee2017 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/256">#256</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-4">
+<h2>Version - 0.4.4<a class="headerlink" href="#version-0-4-4" title="Permalink to this heading">#</a></h2>
+<section id="id13">
+<h3>Enhancements<a class="headerlink" href="#id13" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Add TRCA algorithm for SSVEP (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/238">#238</a> by <a class="reference external" href="https://github.com/ludovicdmt">Ludovic Darmet</a>)</p></li>
+</ul>
+</section>
+<section id="id14">
+<h3>Bugs<a class="headerlink" href="#id14" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Remove unused argument from dataset_search (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/243">#243</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
+<li><p>Remove MNE call to <cite>_fetch_dataset</cite> and use MOABB <cite>_fetch_file</cite> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/235">#235</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Correct doc formatting (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/232">#232</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id15">
+<h3>API changes<a class="headerlink" href="#id15" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Minimum supported Python version is now 3.7</p></li>
+<li><p>MOABB now depends on scikit-learn &gt;= 1.0</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-3">
+<h2>Version - 0.4.3<a class="headerlink" href="#version-0-4-3" title="Permalink to this heading">#</a></h2>
+<section id="id16">
+<h3>Enhancements<a class="headerlink" href="#id16" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Rewrite Lee2019 to add P300 and SSVEP datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/217">#217</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+</ul>
+</section>
+<section id="id17">
+<h3>Bugs<a class="headerlink" href="#id17" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Avoid information leakage for MNE Epochs pipelines in evaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/222">#222</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Correct error in set_download_dir (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/225">#225</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Ensure that channel order is consistent across dataset when channel argument is specified in paradigm (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/229">#229</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id18">
+<h3>API changes<a class="headerlink" href="#id18" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>ch_names argument added to init of moabb.datasets.fake.FakeDataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/229">#229</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-2">
+<h2>Version - 0.4.2<a class="headerlink" href="#version-0-4-2" title="Permalink to this heading">#</a></h2>
+<section id="id19">
+<h3>Enhancements<a class="headerlink" href="#id19" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+<section id="id20">
+<h3>Bugs<a class="headerlink" href="#id20" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Correct error when downloading Weibo dataset  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/212">#212</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id21">
+<h3>API changes<a class="headerlink" href="#id21" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-1">
+<h2>Version - 0.4.1<a class="headerlink" href="#version-0-4-1" title="Permalink to this heading">#</a></h2>
+<section id="id22">
+<h3>Enhancements<a class="headerlink" href="#id22" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+<section id="id23">
+<h3>Bugs<a class="headerlink" href="#id23" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Correct path error for first time launch (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/204">#204</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix optional dependencies issues for PyPi (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/205">#205</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id24">
+<h3>API changes<a class="headerlink" href="#id24" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Remove update_path on all datasets, <cite>update_path</cite> parameter in <cite>dataset.data_path()</cite> is deprecated (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/207">#207</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-4-0">
+<h2>Version - 0.4.0<a class="headerlink" href="#version-0-4-0" title="Permalink to this heading">#</a></h2>
+<section id="id25">
+<h3>Enhancements<a class="headerlink" href="#id25" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Implementation for learning curves (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/155">#155</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Adding Neiry Demons P300 dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/156">#156</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+<li><p>Coloredlogs removal (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/163">#163</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+<li><p>Update for README (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/164">#164</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Test all relevant python versions in Github Actions CI (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/167">#167</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+<li><p>Adding motor imagery part of the Lee2019 dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/170">#170</a> by <a class="reference external" href="https://github.com/AliAbdulHussain">Ali Abdul Hussain</a>)</p></li>
+<li><p>CI: deploy docs from CI pipeline  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/124">#124</a> by <a class="reference external" href="https://github.com/ErikBjare">Erik Bjäreholt</a>, <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Remove dependencies: WFDB and pyunpack (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/180">#180</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add support for FigShare API (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>New download backend function relying on Pooch, handling FTP, HTTP and HTTPS (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Complete rework of examples and tutorial (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Change default storage location for results: instead of moabb source code directory it is now stored in mne_data (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Major update of test (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding troubleshooting and badges in README (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/189">#189</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Use MNE epoch in evaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/192">#192</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Allow changing of storage location (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/192">#192</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Deploy docs on moabb.github.io (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/196">#196</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Broadening subject_list type for <code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.BaseDataset()</span></code> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/198">#198</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding this what’s new (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/200">#200</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Improving cache usage and save computation time in CI (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/200">#200</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Rewrite Lee2019 to add P300 and SSVEP datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/217">#217</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+</ul>
+</section>
+<section id="id26">
+<h3>Bugs<a class="headerlink" href="#id26" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Restore basic logging (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/177">#177</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Correct wrong type of results dataframe columns (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/188">#188</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">accept</span></code> arg to acknowledge licence for <a class="reference internal" href="generated/moabb.datasets.Shin2017A.html#moabb.datasets.Shin2017A" title="moabb.datasets.Shin2017A"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.Shin2017A()</span></code></a> and <a class="reference internal" href="generated/moabb.datasets.Shin2017B.html#moabb.datasets.Shin2017B" title="moabb.datasets.Shin2017B"><code class="xref py py-func docutils literal notranslate"><span class="pre">moabb.datasets.Shin2017B()</span></code></a> (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/201">#201</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id27">
+<h3>API changes<a class="headerlink" href="#id27" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Drop <cite>update_path</cite> from moabb.download.data_path and moabb.download.data_dl</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-3-0">
+<h2>Version 0.3.0<a class="headerlink" href="#version-0-3-0" title="Permalink to this heading">#</a></h2>
+<section id="id28">
+<h3>Enhancements<a class="headerlink" href="#id28" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Expose sklearn error_score parameter (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/70">#70</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Adds a <code class="docutils literal notranslate"><span class="pre">unit_factor</span></code> attribute to base_paradigms (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/72">#72</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Allow event lists in P300 paradigm (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/83">#83</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Return epochs instead of np.ndarray in process_raw (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/86">#86</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Set path for hdf5 files (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/92">#92</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Update to MNE 0.21  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/101">#101</a> by <a class="reference external" href="https://github.com/ragatti">Ramiro Gatti</a> and <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding a baseline correction (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/115">#115</a> by <a class="reference external" href="https://github.com/ragatti">Ramiro Gatti</a>)</p></li>
+<li><p>Adding SSVEP datasets: MAMEM1, MAMEM2, MAMEM3, Nakanishi2015, Wang2016, (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/118">#118</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>, <a class="reference external" href="https://github.com/qbarthelemy">Quentin Barthelemy</a>, and <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
+<li><p>Switch to GitHub Actions (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/124">#124</a> by <a class="reference external" href="https://github.com/ErikBjare">Erik Bjäreholt</a>)</p></li>
+<li><p>Allow recording of additional scores/parameters/metrics in evaluation (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/127">#127</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/128">#128</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Fix Ofner2017 and PhysionetMI annotations (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/135">#135</a> by <a class="reference external" href="https://github.com/AliAbdulHussain">Ali Abdul Hussain</a>)</p></li>
+<li><p>Adding Graz workshop tutorials (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/130">#130</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/137">#137</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a> and <a class="reference external" href="https://github.com/lucascust">Lucas Custódio</a>)</p></li>
+<li><p>Adding pre-commit configuration using isort, black and flake8 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/140">#140</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+<li><p>style: format Python code with black  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/147">#147</a> by <a class="reference external" href="https://github.com/ErikBjare">Erik Bjäreholt</a>)</p></li>
+<li><p>Switching to Poetry dependency management (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/150">#150</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+<li><p>Using Prettier to format md and yml files (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/151">#151</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+</ul>
+</section>
+<section id="id29">
+<h3>Bugs<a class="headerlink" href="#id29" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Use stim_channels or check annotation when loading files in Paradigm  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/72">#72</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Correct MNE issues (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/76">#76</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix capitalization in channel names of cho dataset  (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/90">#90</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Correct failing CI tests (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/100">#100</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix EPFL dataset flat signal sections and wrong scaling (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/104">#104</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/96">#96</a> by  <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Fix schirrmeister dataset for Python3.8 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/105">#105</a> by <a class="reference external" href="https://github.com/robintibor">Robin Schirrmeister</a>)</p></li>
+<li><p>Correct event detection problem and duplicate event error (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/106">#106</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix channel selection in paradigm (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/108">#108</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix upperlimb Ofner2017 error and gdf import problem (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/111">#111</a> and <a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/110">#110</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Fix event_id in events_from_annotations missed (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/112">#112</a> by <a class="reference external" href="https://github.com/ragatti">Ramiro Gatti</a>)</p></li>
+<li><p>Fix h5py&gt;=3.0 compatibility issue (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/138">#138</a> by <a class="reference external" href="https://github.com/m2-farzan">Mohammad Mostafa Farzan</a>)</p></li>
+<li><p>Python 2 support removal (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/148">#148</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+<li><p>Travis-ci config removal (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/149">#149</a> by <a class="reference external" href="https://github.com/v-goncharenko">Vladislav Goncharenko</a>)</p></li>
+</ul>
+</section>
+<section id="id30">
+<h3>API changes<a class="headerlink" href="#id30" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-2-1">
+<h2>Version 0.2.1<a class="headerlink" href="#version-0-2-1" title="Permalink to this heading">#</a></h2>
+<section id="id31">
+<h3>Enhancements<a class="headerlink" href="#id31" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Add Tikhonov regularized CSP in <code class="docutils literal notranslate"><span class="pre">moabb.pipelines.csp</span></code> from the paper (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/60">#60</a> by <a class="reference external" href="https://github.com/vinay-jayaram">Vinay Jayaram</a>)</p></li>
+<li><p>update to MNE version 0.19 (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/73">#73</a> by <a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Improve doc building in CI (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/60">#60</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id32">
+<h3>Bugs<a class="headerlink" href="#id32" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Update GigaDB Cho2017 URL (<a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a> and <a class="reference external" href="https://github.com/vinay-jayaram">Vinay Jayaram</a>)</p></li>
+<li><p>Fix braininvaders ERP data (<a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a>)</p></li>
+<li><p>Replace MNE <code class="docutils literal notranslate"><span class="pre">read_montage</span></code> with <code class="docutils literal notranslate"><span class="pre">make_standard_montage</span></code> (<a class="reference external" href="https://github.com/jsosulski">Jan Sosulski</a>)</p></li>
+<li><p>Correct Flake and PEP8 error (<a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id33">
+<h3>API changes<a class="headerlink" href="#id33" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-2-0">
+<h2>Version 0.2.0<a class="headerlink" href="#version-0-2-0" title="Permalink to this heading">#</a></h2>
+<section id="id34">
+<h3>Enhancements<a class="headerlink" href="#id34" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>MOABB corresponding to the paper version by <a class="reference external" href="https://github.com/vinay-jayaram">Vinay Jayaram</a> and <a class="reference external" href="https://github.com/alexandrebarachant">Alexandre Barachant</a></p></li>
+<li><p>Creating P300 paradigm and BNCI datasets (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/53">#53</a> by <a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a>)</p></li>
+<li><p>Adding EPFL P300 dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/56">#56</a> by <a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a>)</p></li>
+<li><p>Adding BrainInvaders P300 dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/57">#57</a> by <a class="reference external" href="https://github.com/plcrodrigues">Pedro L. C. Rodrigues</a>)</p></li>
+<li><p>Creating SSVEP paradigm and SSVEPExo dataset (<a class="reference external" href="https://github.com/NeuroTechX/moabb/issues/59">#59</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+</ul>
+</section>
+<section id="id35">
+<h3>Bugs<a class="headerlink" href="#id35" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
+<section id="id36">
+<h3>API changes<a class="headerlink" href="#id36" title="Permalink to this heading">#</a></h3>
+<ul class="simple">
+<li><p>None</p></li>
+</ul>
+</section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id18">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-4-2">Version - 0.4.2</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id19">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id20">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id21">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-4-1">Version - 0.4.1</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id22">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id23">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id24">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-4-0">Version - 0.4.0</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id25">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id26">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id27">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-3-0">Version 0.3.0</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id28">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id29">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id30">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-2-1">Version 0.2.1</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id31">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id32">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id33">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-2-0">Version 0.2.0</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id34">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id35">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id36">API changes</a></li>
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