torch-CLEEGN

This repository is the official PyTorch implementation of "CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction".

Instruction

Environment Setup

conda create --name ENV_NAME python=3.7
conda activate ENV_NAME
nvidia-smi  # Get the CUDA Version
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Check GPU Support

import torch
print(torch.cuda.is_available())
print(torch.cuda.device_count())
dev_id = torch.cuda.current_device()
print(torch.cuda.get_device_name(dev_id))

Training Usage

$ python main.py configs/CONFIG_FILENAME.json

Quick Inference

$ python inference.py --mat-path sample/ERN_S02_exSam.mat --model-path weights/weight-0.pth

Results

Real-time demo

cemeteryparty/real_time_LSL_BCI is the implementation of CLEEGN in a real-time system. The device in the video is an 8-channel EEG cap designed by Artise Biomedical Co., Ltd.

a541b67d5e1c364b-20240108dem0.mp4