MiaoZhengQing / TSFF

Description

Thanks Can Han @ SJTU for pointing out the EEGDepthAttention parameter update issue, which has been fixed.
Uploaded the model from the paper.
I will compare the effect of DepthAttention module based on backpropagation and DepthAttention module without backpropagation on the results of different datasets, and the results will be uploaded later.

Code for paper, Time-space-frequency feature Fusion for 3-channel motor imagery classification

• 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.

Related works

• This paper is a follow-up version of SDDA and LMDA-Net, the preprocessing method is inherited from SDDA, and the TSFF-raw method is derived from LMDA-Net.
• 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: [e-mail]