MIN2Net
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
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. The project is written primarily in Python, distributed under the Apache License 2.0 license, first published in 2021. Key topics include: ae, autoencoder, bci, brain-computer-interface, deep-learning.
<img src="https://min2net.github.io/assets/images/min2net-logo.png" width="30%" height="30%">
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
π PyTorch Version π
The MIN2Net architecture implemented in PyTorch is available on:
π AlphaGrad Repository β mtl_bci/networks/MIN2Net.py
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
condaenvironment with dependencies
bashwget https://raw.githubusercontent.com/IoBT-VISTEC/MIN2Net/main/environment.yml conda env create -f environment.yml conda activate min2net
Installation:
- Using pip
bashpip install min2net
- Using the released python wheel
bashwget 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
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 Rangpong, Phurin and Kiatthaveephong, Suktipol 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={2022},
volume={69},
number={6},
pages={2105-2118},
doi={10.1109/TBME.2021.3137184}}
License
Copyright Β© 2021-All rights reserved by INTERFACES (BRAIN lab @ IST, VISTEC, Thailand).
Distributed by an Apache License 2.0.
Contributors
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