/BCI-ToolBox

Deep Learning pipeline for motor-imagery classification.

Primary LanguagePythonMIT LicenseMIT

BCI-ToolBox

1. Introduction

BCI-ToolBox is deep learning pipeline for motor-imagery classification.
This repo contains five models: ShallowConvNet, DeepConvNet, EEGNet, FBCNet, BCI2021.
(BCI2021 is not an official name.)

2. Installation

Environment

  • Python == 3.7.10
  • PyTorch == 1.9.0
  • mne == 0.23.0
  • braindecode == 0.5.1
  • CUDA == 11.0

Create conda environment

conda install pytorch=1.9.0 cudatoolkit=11.1 -c pytorch -c nvidia
conda install numpy pandas matplotlib pyyaml ipywidgets
pip install torchinfo braindecode moabb mne

3. Directory structure

.
├── README.md
├── base
│   ├── constructor.py
│   └── layers.py
├── configs
│   ├── BCI2021
│   │   └── default.yaml
│   ├── DeepConvNet
│   │   └── default.yaml
│   ├── EEGNet
│   │   └── default.yaml
│   ├── FBCNet
│   │   └── default.yaml
│   ├── ShallowConvNet
│   │   └── default.yaml
│   └── demo
│       ├── arch.yaml
│       ├── bci2021.yaml
│       ├── test.yaml
│       ├── train.yaml
│       └── training_params.yaml
├── data_loader
│   ├── data_generator.py
│   ├── datasets
│   │   ├── __init__.py
│   │   ├── bnci2014.py
│   │   ├── cho2017.py
│   │   ├── folder_dataset.py
│   │   ├── openbmi.py
│   │   └── tmp_dataset.py
│   └── transforms.py
├── main.py
├── models
│   ├── BCI2021
│   │   ├── BCI2021.py
│   │   └── __init__.py
│   ├── DeepConvNet
│   │   ├── DeepConvNet.py
│   │   └── __init__.py
│   ├── EEGNet
│   │   ├── EEGNet.py
│   │   └── __init__.py
│   ├── FBCNet
│   │   ├── FBCNet.py
│   │   └── __init__.py
│   ├── ShallowConvNet
│   │   ├── ShallowConvNet.py
│   │   └── __init__.py
│   ├── __init__.py
│   └── model_builder.py
├── trainers
│   ├── __init__.py
│   ├── cls_trainer.py
│   └── trainer_maker.py
└── utils
    ├── calculator.py
    ├── painter.py
    └── utils.py

4. Dataset

5. Get Started

Create wandb_key.yaml file

  • Create wandb_key.yaml file in configs directory. 
    # wandb_key.yaml
key: WANDB API keys
  • WANDB API keys can be obtained from your W&B account settings.

train

Use W&B

python main.py --config_file=configs/demo/train.yaml

Not use W&B

python main.py --config_file=configs/demo/train.yaml --no_wandb

USE GPU

python main.py --config_file=configs/demo/train.yaml --device=0  # Use GPU 0
python main.py --config_file=configs/demo/train.yaml --device=1  # Use GPU 1
python main.py --config_file=configs/demo/train.yaml --device=2  # Use GPU 2
  • GPU numbers depend on your server.

USE Sweep

# W&B
sweep_file: configs/demo/training_params.yaml
project: Demo
tags: [train]
  • Add this block to config file for finding training parameters.
# W&B
sweep_file: configs/demo/arch.yaml
sweep_type: arch
project: Demo
tags: [train]
  • Add this block to config file for finding model architecture.

test

python main.py --config_file=configs/demo/test.yaml

5. References