Description

Thanks Can Han @ SJTU for pointing out the EEGDepthAttention parameter update issue, which has been fixed.

Code for paper, LMDA-Net: A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability

• Provided the model required in the paper
• Code required for the interpretable algorithms used in the paper is provided
• Unoptimized code, under continuous updates.

Requirements

• Python == 3.6 or higher
• Pytorch == 1.10 or higher
• GPU is required.

Models Implemented

• LMDA-Net
• EEGNet
• ShallowConvNet

Related works

• This paper is a follow-up version of SDDA, the preprocessing method is inherited from SDDA.
• This paper is followed by the work of Time-space-frequency feature Fusion for 3-channel motor imagery classification, which investigates the application of time-space-frequency feature fusion methods to 3-lead motion imagery. Code of TSFF
• SDDA, LMDA-Net and Time-space-frequency feature Fusion for 3-channel motor imagery classification are all under review, the arxiv is an early version, the final manuscript will be different.

Paper Citation

If you use this code in a scientific publication, please cite us as:
% TSFF-Net
Miao Z, Zhao M. Time-space-frequency feature Fusion for 3-channel motor imagery classification[J]. arXiv preprint arXiv:2304.01461, 2023.

% LMDA-Net
Miao Z, Zhang X, Zhao M, et al. LMDA-Net: A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability[J]. arXiv preprint arXiv:2303.16407, 2023.

% SDDA
Miao Z, Zhang X, Menon C, et al. Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework[J]. arXiv preprint arXiv:2202.09559, 2022.

% TSFF-Net
@article{miao2023time,
  title={Time-space-frequency feature Fusion for 3-channel motor imagery classification},
  author={Miao, Zhengqing and Zhao, Meirong},
  journal={arXiv preprint arXiv:2304.01461},
  year={2023}
}

% LMDA-Net
@article{miao2023lmda,
  title={LMDA-Net: A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability},
  author={Miao, Zhengqing and Zhang, Xin and Zhao, Meirong and Ming, Dong},
  journal={arXiv preprint arXiv:2303.16407},
  year={2023}
}

% SDDA
@article{miao2022priming,
  title={Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework},
  author={Miao, Zhengqing and Zhang, Xin and Menon, Carlo and Zheng, Yelong and Zhao, Meirong and Ming, Dong},
  journal={arXiv preprint arXiv:2202.09559},
  year={2022}
}

Contact

Email: [email protected]