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MIN2Net

End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)

From IoBT-VISTECΒ·Updated April 27, 2026Β·View on GitHubΒ·

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.

Latest release: v1.0.1β€” MIN2Net v1.0.1
February 5, 2022View Changelog β†’

<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

Open In Colab
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DOI

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.

πŸ“Œ PyTorch Version πŸ”

The MIN2Net architecture implemented in PyTorch is available on:

πŸ‘‰ AlphaGrad Repository – 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
  1. Create conda environment with dependencies
bash
wget https://raw.githubusercontent.com/IoBT-VISTEC/MIN2Net/main/environment.yml conda env create -f environment.yml conda activate min2net

Installation:

  1. Using pip
bash
pip install min2net
  1. Using the released python wheel
bash
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

<img src="https://min2net.github.io/assets/images/colab_favicon.ico" width="50" height="50"> 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 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

Showing top 2 contributors by commit count.

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This article is auto-generated from IoBT-VISTEC/MIN2Net via the GitHub API.Last fetched: 6/26/2026