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MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Project description

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


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
  1. 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:

  1. Using pip
$ pip install min2net
  1. Using the released python wheel
$ wget https://github.com/IoBT-VISTEC/MIN2Net/releases/download/v1.0.0/min2net-1.0.0-py3-none-any.whl
$ pip install min2net-1.0.0-py3-none-any.whl

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}}

License

Copyright © 2021-All rights reserved by INTERFACES (BRAIN lab @ IST, VISTEC, Thailand). Distributed by an Apache License 2.0.

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