Deep Learning pipeline for motor-imagery classification.

Last update: Oct 31, 2022

Overview

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

Use braindecode

5. Get Started

We use W&B for experimental management tool.
Example for W&B Dashboard
Example for W&B Report

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

ShallowConvNet [Paper] [Code]
DeepConvNet [Paper] [Code]
EEGNet [Paper] [Code]
FBCNet [Paper] [Code]
BCI2021 [Paper] [Code]

Deep Learning pipeline for motor-imagery classification.

Related tags

Overview

BCI-ToolBox

1. Introduction

2. Installation

Environment

Create conda environment

3. Directory structure

4. Dataset

5. Get Started

Create wandb_key.yaml file

train

test

5. References

Owner

DongHee

[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

Retrieve and analysis data from SDSS (Sloan Digital Sky Survey)

Аналитика доходности инвестиционного портфеля в Тинькофф брокере

Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

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Vehicles Counting using YOLOv4 + DeepSORT + Flask + Ngrok

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

Implementation for Shape from Polarization for Complex Scenes in the Wild

Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"

This is my codes that can visualize the psnr image in testing videos.

Implementation of ToeplitzLDA for spatiotemporal stationary time series data.

HandTailor: Towards High-Precision Monocular 3D Hand Recovery

Fast Neural Style for Image Style Transform by Pytorch

"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

Federated_learning codes used for the the paper "Evaluation of Federated Learning Aggregation Algorithms" and "A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison"

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Finding Donors for CharityML

MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens