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

Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Reference PyTorch implementation of "End-to-end optimized image compression with competition of prior distributions"

Code for our paper: Online Variational Filtering and Parameter Learning

The official implementation for ACL 2021 "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval".

The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

DeiT: Data-efficient Image Transformers

AgeGuesser: deep learning based age estimation system. Powered by EfficientNet and Yolov5

FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

Reinforcement learning algorithms in RLlib

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Real-time multi-object tracker using YOLO v5 and deep sort

Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

PyTorch code for: Learning to Generate Grounded Visual Captions without Localization Supervision

The code for our paper submitted to RAL/IROS 2022: OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition.

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021