-

What’s new

+

What’s new

+ +
+

v0.5.1 (August 2021)

+

This is a bugfix release. The latest pre-trained classifiers for yasa.SleepStaging were accidentally missing from the previous release. They have now been included in this release.

+
-

v0.5.0 (August 2021)

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v0.5.0 (August 2021)

This is a major release with an important bugfix for the slow-waves detection as well as API-breaking changes in the automatic sleep staging module. We recommend all users to upgrade to this version with pip install –upgrade yasa.

Slow-waves detection

We have fixed a critical bug in yasa.sw_detect() in which the detection could keep slow-waves with invalid duration (e.g. several tens of seconds). We have now added extra safety checks to make sure that the total duration of the slow-waves does not exceed the maximum duration allowed by the dur_neg and dur_pos parameters (default = 2.5 seconds).

@@ -118,7 +147,7 @@

v0.5.0 (August 2021)

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v0.4.1 (March 2021)

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v0.4.1 (March 2021)

New functions

  1. Added yasa.topoplot(), a wrapper around mne.viz.plot_topomap(). See 15_topoplot.ipynb

    2. @@ -134,7 +163,7 @@

    v0.4.1 (March 2021)

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v0.4.0 (November 2020)

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v0.4.0 (November 2020)

This is a major release with several new functions, the biggest of which is the addition of an automatic sleep staging module (yasa.SleepStaging). This means that YASA can now automatically score the sleep stages of your raw EEG data. The classifier was trained and validated on more than 3000 nights from the National Sleep Research Resource (NSRR) website.

Briefly, the algorithm works by calculating a set of features for each 30-sec epochs from a central EEG channel (required), as well as an EOG channel (optional) and an EMG channel (optional). For best performance, users can also specify the age and the sex of the participants. Pre-trained classifiers are already included in YASA. The automatic sleep staging algorithm requires the LightGBM and antropy package.

Other changes

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v0.4.0 (November 2020)

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v0.3.0 (May 2020)

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v0.3.0 (May 2020)

This is a major release with several API-breaking changes in the spindles, slow-waves and REMs detection.

First, the yasa.spindles_detect_multi() and yasa.sw_detect_multi() have been removed. Instead, the yasa.spindles_detect() and yasa.sw_detect() functions can now handle both single and multi-channel data.

Second, I was getting some feedback that it was difficult to get summary statistics from the detection dataframe. For instance, how can you get the average duration of the detected spindles, per channel and/or per stage? Similarly, how can you get the slow-waves count and density per stage and channel? To address these issues, I’ve now modified the output of the yasa.spindles_detect(), yasa.sw_detect() and yasa.rem_detect() functions, which is now a class (= object) and not a simple Pandas DataFrame. The advantage is that the new output allows you to quickly get the raw data or summary statistics grouped by channel and/or sleep stage using the .summary() method.

@@ -187,7 +216,7 @@

v0.3.0 (May 2020)

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v0.2.0 (April 2020)

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v0.2.0 (April 2020)

This is a major release with several new functions, bugfixes and miscellaneous enhancements in existing functions.

Bugfixes

    @@ -224,7 +253,7 @@

    v0.2.0 (April 2020)

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v0.1.9 (February 2020)

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v0.1.9 (February 2020)

New functions

  1. Added yasa.transition_matrix() to calculate the state-transition matrix of an hypnogram.

    2. @@ -244,7 +273,7 @@

    v0.1.9 (February 2020)

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v0.1.8 (October 2019)

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v0.1.8 (October 2019)

  1. Added yasa.plot_spectrogram() function.

  2. Added lspopt in the dependencies.

    3. @@ -254,7 +283,7 @@

    v0.1.8 (October 2019)

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v0.1.7 (August 2019)

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v0.1.7 (August 2019)

  1. Added yasa.sliding_window() function.

  2. Added yasa.irasa() function.

    3. @@ -263,7 +292,7 @@

    v0.1.7 (August 2019)

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v0.1.6 (August 2019)

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v0.1.6 (August 2019)

  1. Added bandpower function

  2. One can now directly pass a raw MNE object in several multi-channel functions of YASA, instead of manually passing data, sf, and ch_names. YASA will automatically convert MNE data from Volts to uV, and extract the sampling frequency and channel names. Examples of this can be found in the Jupyter notebooks examples.

    3. @@ -271,7 +300,7 @@

    v0.1.6 (August 2019)

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v0.1.5 (August 2019)

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v0.1.5 (August 2019)

  1. Added REM detection (rem_detect) on LOC and ROC EOG channels + example notebook

  2. Added yasa/hypno.py file, with several functions to load and upsample sleep stage vector (hypnogram).

    3. @@ -280,14 +309,14 @@

    v0.1.5 (August 2019)

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v0.1.4 (May 2019)

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v0.1.4 (May 2019)

  1. Added get_sync_sw function to get the synchronized timings of landmarks timepoints in slow-wave sleep. This can be used in combination with seaborn.lineplot to plot an average template of the detected slow-wave, per channel.

-

v0.1.3 (March 2019)

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v0.1.3 (March 2019)

  1. Added slow-waves detection for single and multi channel

  2. Added include argument to select which values of hypno should be used as a mask.

    3. @@ -299,7 +328,7 @@

    v0.1.3 (March 2019)

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v0.1.2 (February 2019)

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v0.1.2 (February 2019)

  1. Added support for multi-channel detection via spindles_detect_multi function.

  2. Added support for hypnogram mask

    3. @@ -309,7 +338,7 @@

    v0.1.2 (February 2019)

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v0.1.1 (January 2019)

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v0.1.1 (January 2019)

  1. Added post-processing Isolation Forest

  2. Updated Readme and added support with Visbrain

    3. @@ -318,7 +347,7 @@

    v0.1.1 (January 2019)

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v0.1 (December 2018)

+

v0.1 (December 2018)

Initial release of YASA: basic spindles detection.

diff --git a/docs/build/html/contributing.html b/docs/build/html/contributing.html index 88dee7e..9f1124a 100644 --- a/docs/build/html/contributing.html +++ b/docs/build/html/contributing.html @@ -3,7 +3,7 @@ - Contribute to YASA — yasa 0.5.0 documentation + Contribute to YASA — yasa 0.5.1 documentation @@ -38,7 +38,7 @@ yasa - 0.5.0 + 0.5.1