Metadata-Version: 2.1
Name: min2net
Version: 1.0.1
Summary: MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
Home-page: https://MIN2Net.github.io
Author: INTERFACES
Author-email: IoBT.VISTEC@gmail.com
License: Apache Software License
Download-URL: https://github.com/IoBT-VISTEC/MIN2Net/releases
Project-URL: Bug Tracker, https://github.com/IoBT-VISTEC/MIN2Net/issues
Project-URL: Documentation, https://MIN2Net.github.io
Project-URL: Source Code, https://github.com/IoBT-VISTEC/MIN2Net
Description: [<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)
          
        ---
        
        ## 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/v1.0.0/min2net-1.0.0-py3-none-any.whl
          pip install min2net-1.0.0-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 
          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 &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).
        
Keywords: Brain-computer InterfacesBCI,Motor Imagery,MI,Multi-task Learning,Deep Metric Learning,DML,Autoencoder,AE,EEG Classifier
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
