MIN2Net

End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Python API and the novel algorithm for motor imagery EEG recognition named MIN2Net. The API benefits BCI researchers ranging from beginners to experts. We demonstrate the examples in using the API for loading benchmark datasets, preprocessing, training, and validation of SOTA models, including MIN2Net. In summary, the API allows the researchers to construct the pipeline for benchmarking the newly proposed models and very recently developed SOTA models.

Website: https://min2net.github.io
Documentation: https://min2net.github.io
Source code: https://github.com/IoBT-VISTEC/MIN2Net
Bug reports: https://github.com/IoBT-VISTEC/MIN2Net/issues

Getting started

Dependencies

Python==3.6.9
tensorflow-gpu==2.2.0
tensorflow-addons==0.9.1
scikit-learn>=0.24.1
wget>=3.2

Create conda environment with dependencies

$ wget https://raw.githubusercontent.com/IoBT-VISTEC/MIN2Net/main/environment.yml
$ conda env create -f environment.yml
$ conda activate min2net

Installation:

Using pip

  $ pip install min2net

Using the released python wheel

  $ wget https://github.com/IoBT-VISTEC/MIN2Net/releases/download/v1.0.1/min2net-1.0.1-py3-none-any.whl
  $ pip install min2net-1.0.1-py3-none-any.whl

Tutorial

Open in Colab

Citation

To cited our paper

P. Autthasan et al., “MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification,” in IEEE Transactions on Biomedical Engineering, doi: 10.1109/TBME.2021.3137184.

@ARTICLE{9658165,
  author={Autthasan, Phairot and Chaisaen, Rattanaphon and Sudhawiyangkul, Thapanun and 
  Kiatthaveephong, Suktipol and Rangpong, Phurin and Dilokthanakul, Nat 
  and Bhakdisongkhram, Gun and Phan, Huy and Guan, Cuntai and 
  Wilaiprasitporn, Theerawit},
  journal={IEEE Transactions on Biomedical Engineering}, 
  title={MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery 
  EEG Classification}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TBME.2021.3137184}}

Source Code

View source on GitHub