<img src="imgs/MixNet_logo_1.png" width="40%" height="40%">
### MixNet: Joining Force of Classical and Modern Approaches toward The Comprehensive Pipeline in Motor Imagery EEG Classification
[](https://pypi.org/project/mixnet-bci/)
[](https://ieeexplore.ieee.org/document/10533256)
Python API and the novel algorithm for motor imagery EEG classification named MixNet. The API benefits BCI researchers ranging from beginners to experts. We demonstrate examples of using the API for loading six benchmark datasets, preprocessing, training, and validating SOTA models, including MixNet. In summary, the API allows the researchers to construct the pipeline to benchmark the newly proposed and recently developed SOTA models.
- **Website:** [https://max-phairot-a.github.io/mixnet.github.io](https://max-phairot-a.github.io/mixnet.github.io)
- **Documentation:** [https://max-phairot-a.github.io/mixnet.github.io](https://max-phairot-a.github.io/mixnet.github.io)
- **Source code:** [https://github.com/Max-Phairot-A/MixNet](https://github.com/Max-Phairot-A/MixNet)
- **Bug reports:** [https://github.com/Max-Phairot-A/MixNet/issues](https://github.com/Max-Phairot-A/MixNet/issues)
---
## Getting started
### Dependencies
- Python==3.8.10
- tensorflow-gpu==2.7.0
- tensorflow-addons==0.16.1
- scikit-learn>=1.2.2
- wget>=3.2
- h5py==3.5.0
- pandas>=2.0
1. Create `docker container` with dependencies
```bash
docker pull tensorflow/tensorflow:2.7.0-gpu
docker run -ti --name mixnet_container docker.io/tensorflow/tensorflow:2.7.0-gpu bash
wget https://github.com/Max-Phairot-A/MixNet/blob/main/requirement.txt
pip install -r requirements.txt
```
### Installation:
###
1. Using pip
```bash
pip install mixnet-bci
```
2. Using the released python wheel
```bash
wget https://github.com/Max-Phairot-A/MixNet/releases/tag/v1.0.0/mixnet_bci-1.0.0-py3-none-any.whl
pip install mixnet_bci-1.0.0-py3-none-any.whl
```
### Citation
To read & cite [our paper](https://ieeexplore.ieee.org/document/10533256)
P. Autthasan, R. Chaisaen, H. Phan, M. D. Vos and T. Wilaiprasitporn, "MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification," in IEEE Internet of Things Journal, vol. 11, no. 17, pp. 28539-28554, 1 Sept.1, 2024, doi: 10.1109/JIOT.2024.3402254.
```
@ARTICLE{10533256,
author={Autthasan, Phairot and Chaisaen, Rattanaphon and Phan, Huy and Vos, Maarten De and Wilaiprasitporn, Theerawit},
journal={IEEE Internet of Things Journal},
title={MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification},
year={2024},
volume={11},
number={17},
pages={28539-28554},
keywords={Electroencephalography;Task analysis;Feature extraction;Measurement;Internet of Things;Multitasking;Motors;Adaptive gradient blending;brain-computer interface (BCI);deep learning (DL);motor imagery (MI);multitask learning},
doi={10.1109/JIOT.2024.3402254}}
```
### License
Copyright © 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/Max-Phairot-A/MixNet/blob/main/LICENSE).
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"description": "<img src=\"imgs/MixNet_logo_1.png\" width=\"40%\" height=\"40%\">\n\n### MixNet: Joining Force of Classical and Modern Approaches toward The Comprehensive Pipeline in Motor Imagery EEG Classification\n\n[](https://pypi.org/project/mixnet-bci/)\n[](https://ieeexplore.ieee.org/document/10533256)\n\n\nPython API and the novel algorithm for motor imagery EEG classification named MixNet. The API benefits BCI researchers ranging from beginners to experts. We demonstrate examples of using the API for loading six benchmark datasets, preprocessing, training, and validating SOTA models, including MixNet. In summary, the API allows the researchers to construct the pipeline to benchmark the newly proposed and recently developed SOTA models.\n\n- **Website:** [https://max-phairot-a.github.io/mixnet.github.io](https://max-phairot-a.github.io/mixnet.github.io)\n- **Documentation:** [https://max-phairot-a.github.io/mixnet.github.io](https://max-phairot-a.github.io/mixnet.github.io)\n- **Source code:** [https://github.com/Max-Phairot-A/MixNet](https://github.com/Max-Phairot-A/MixNet)\n- **Bug reports:** [https://github.com/Max-Phairot-A/MixNet/issues](https://github.com/Max-Phairot-A/MixNet/issues)\n---\n\n## Getting started\n\n### Dependencies\n\n- Python==3.8.10\n- tensorflow-gpu==2.7.0\n- tensorflow-addons==0.16.1\n- scikit-learn>=1.2.2\n- wget>=3.2\n- h5py==3.5.0\n- pandas>=2.0\n\n1. Create `docker container` with dependencies\n```bash\ndocker pull tensorflow/tensorflow:2.7.0-gpu\ndocker run -ti --name mixnet_container docker.io/tensorflow/tensorflow:2.7.0-gpu bash\nwget https://github.com/Max-Phairot-A/MixNet/blob/main/requirement.txt\npip install -r requirements.txt\n```\n\n### Installation:\n\n###\n1. Using pip\n\n ```bash\n pip install mixnet-bci\n ```\n2. Using the released python wheel\n\n ```bash\n wget https://github.com/Max-Phairot-A/MixNet/releases/tag/v1.0.0/mixnet_bci-1.0.0-py3-none-any.whl\n pip install mixnet_bci-1.0.0-py3-none-any.whl\n ```\n\n### Citation\n\nTo read & cite [our paper](https://ieeexplore.ieee.org/document/10533256)\n\nP. Autthasan, R. Chaisaen, H. Phan, M. D. Vos and T. Wilaiprasitporn, \"MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification,\" in IEEE Internet of Things Journal, vol. 11, no. 17, pp. 28539-28554, 1 Sept.1, 2024, doi: 10.1109/JIOT.2024.3402254. \n\n```\n@ARTICLE{10533256,\n author={Autthasan, Phairot and Chaisaen, Rattanaphon and Phan, Huy and Vos, Maarten De and Wilaiprasitporn, Theerawit},\n journal={IEEE Internet of Things Journal}, \n title={MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification}, \n year={2024},\n volume={11},\n number={17},\n pages={28539-28554},\n keywords={Electroencephalography;Task analysis;Feature extraction;Measurement;Internet of Things;Multitasking;Motors;Adaptive gradient blending;brain-computer interface (BCI);deep learning (DL);motor imagery (MI);multitask learning},\n doi={10.1109/JIOT.2024.3402254}}\n```\n\n### License\nCopyright © 2021-All rights reserved by [INTERFACES (BRAIN lab @ IST, VISTEC, Thailand)](https://www.facebook.com/interfaces.brainvistec).\nDistributed by an [Apache License 2.0](https://github.com/Max-Phairot-A/MixNet/blob/main/LICENSE).\n\n\n",
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