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XAI606

This repo contains the non-official implementation of EEGNet pytorch version.

1. Installation

Environment

  • Python == 3.7.10
  • Pytorch == 1.9.0
  • CUDA 11.0

Dependencies

Create conda environment

  • conda == 4.10.1

(Option 1) Using yaml file

conda env create --file xai606.yaml

(Option 2) Install packages manually

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

2. Directory structure

.
├── README.md
├── base
├── base_trainer.py
│   └── layers.py
├── configs
│   └── EEGNet_config.yaml
├── data_loader
│   ├── __pycache__
│   ├── data_generator.py
│   └── dataset
├── figures
│   ├── dataset.png
│   └── directory_structure.png
├── history.ipynb
├── main.py
├── models
│   ├── EEGNet_model.py
│   └── model_builder.py
├── runs
│   ├── evaluation.sh
│   ├── prediction.sh
│   └── train.sh
├── trainers
│   ├── EEGNet_trainer.py
│   └── trainer_maker.py
├── utils
│   ├── __pycache__
│   ├── calculator.py
│   ├── get_args.py
│   ├── logger.py
│   └── utils.py
└── xai606.yaml

3. Dataset

.
├── test
│   ├── S01_X.npy
│   ├── S02_X.npy
│   ├── S03_X.npy
│   ├── S04_X.npy
│   ├── S05_X.npy
│   ├── S06_X.npy
│   ├── S07_X.npy
│   ├── S08_X.npy
│   ├── S09_X.npy
├── train
│   ├── S01_X.npy
│   ├── S01_y.npy
│   ├── S02_X.npy
│   ├── S02_y.npy
│   ├── S03_X.npy
│   ├── S03_y.npy
│   ├── S04_X.npy
│   ├── S04_y.npy
│   ├── S05_X.npy
│   ├── S05_y.npy
│   ├── S06_X.npy
│   ├── S06_y.npy
│   ├── S07_X.npy
│   ├── S07_y.npy
│   ├── S08_X.npy
│   ├── S08_y.npy
│   ├── S09_X.npy
│   └── S09_y.npy
└── val
    ├── S01_X.npy
    ├── S01_y.npy
    ├── S02_X.npy
    ├── S02_y.npy
    ├── S03_X.npy
    ├── S03_y.npy
    ├── S04_X.npy
    ├── S04_y.npy
    ├── S05_X.npy
    ├── S05_y.npy
    ├── S06_X.npy
    ├── S06_y.npy
    ├── S07_X.npy
    ├── S07_y.npy
    ├── S08_X.npy
    ├── S08_y.npy
    ├── S09_X.npy
    └── S09_y.npy

BCI Competition IV-2a dataset

  • 9 subjects
  • Classes: left hand, right hand, feet, tongue (4 classes)
  • Session-to-session set up (=subject dependent)
  • Training set: 216 trials per subject
  • Validation set: 72 trials per subject
  • Test set: 288 trials per subject

Preprocessing

  • Sampling rate: 250Hz
  • Time segment: [0.5, 2.5]s post-cue
  • Band-pass filtering: 0-38Hz
  • Normalization: exponential moving average

4. Experiments

Models S01 S02 S03 S04 S05 S06 S07 S08 S09 Mean
EEGNet 76.74 54.51 79.17 54.51 63.19 57.64 83.68 75.00 68.40 68.09

5. Get started

Train

sh runs/train.sh

Prediction

sh runs/prediction.sh

Visualization

  • Please note that history.ipynb file

6. Submission

  • ./result/{save_dir}/{sub_dir}/prediction 폴더를 이메일로 제출해주세요.
  • 혹은 S01~S09 각각의 prediction이 담겨있는 폴더를 제출해주세요 (확장자 무관).
  • donghee-ko@korea.ac.kr