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

A embed able annotation tool for end to end cross document co-reference

利用yolov5和TensorRT从0到1实现目标检测的模型训练到模型部署全过程

The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".

Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

PyTorch implementation of SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching

PyTorch implementation of Pointnet2/Pointnet++

Computer Vision and Pattern Recognition, NUS CS4243, 2022

The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

PyTorch module to use OpenFace's nn4.small2.v1.t7 model

LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021

SemEval2022 Patronizing and Condescending Language (PCL) Detection

OpenMMLab Image and Video Editing Toolbox

Over-the-Air Ensemble Inference with Model Privacy

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Data-driven reduced order modeling for nonlinear dynamical systems

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

Source code for Acorn, the precision farming rover by Twisted Fields