KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #cli #claude-code #codex #developer-tools #dsh-plugin #ai-agent #mcp #deepseek-harness #windows #open-source

MIN2Net

★ 105 Open GitHub ↗

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

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

[<img src="https://min2net.github.io/assets/images/min2net-logo.png" width="30%" height="30%">](https://min2net.github.io)

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

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1IE5J0Yn10ZIhWjSatQn_QWJWZblr6tZy?usp=sharing)
[![Pypi Downloads](https://img.shields.io/pypi/v/min2net?color=green&logo=pypi&logoColor=white)](https://pypi.org/project/min2net)
[![DOI](https://img.shields.io/badge/DOI-10.1109%2FTBME.2021.3137184-blue)](https://ieeexplore.ieee.org/document/9658165)

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](https://min2net.github.io)
- **Documentation:** [https://min2net.github.io](https://min2net.github.io)
- **Source code:** [https://github.com/IoBT-VISTEC/MIN2Net](https://github.com/IoBT-VISTEC/MIN2Net)
- **Bug reports:** [https://github.com/IoBT-VISTEC/MIN2Net/issues](https://github.com/IoBT-VISTEC/MIN2Net/issues)

## 📌 PyTorch Version 🔁

The MIN2Net architecture implemented in PyTorch is available on:

👉 [AlphaGrad Repository](https://github.com/xydxdy/AlphaGrad) – 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
  ```
2. 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](https://colab.research.google.com/drive/1IE5J0Yn10ZIhWjSatQn_QWJWZblr6tZy?usp=sharing)

### Citation

To cited [our paper](https://ieeexplore.ieee.org/document/9658165)

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 &copy; 2021-All rights reserved by [INTERFACES (BRAIN lab @ IST, VISTEC, Thailand)](https://www.facebook.com/interfaces.brainvistec).
Distributed by an [Apache License 2.0](https://github.com/IoBT-VISTEC/MIN2Net/blob/main/LICENSE).

MIN2Net

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README