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

Realtime_Multi-Person_Pose_Estimation

Training deep models using anime, illustration images.

Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

Train a deep learning net with OpenStreetMap features and satellite imagery.

Source code for our paper "Empathetic Response Generation with State Management"

Finite Element Analysis

Bagua is a flexible and performant distributed training algorithm development framework.

Parasite: a tool allowing you to compress and decompress files, to reduce their size

The Habitat-Matterport 3D Research Dataset - the largest-ever dataset of 3D indoor spaces.

Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

Official git repo for the CHIRP project

A repository that finds a person who looks like you by using face recognition technology.

Fully-automated scripts for collecting AI-related papers

UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

Non-Vacuous Generalisation Bounds for Shallow Neural Networks

Library of various Few-Shot Learning frameworks for text classification

Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

Style-based Neural Drum Synthesis with GAN inversion

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)