This is the official PyTorch implementation for our AAAI'24 paper DMMR: Cross-Subject Domain Generalization for EEG-Based Emotion Recognition via Denoising Mixed Mutual Reconstruction
Paper link:

The public available datasets (SEED and SEED-IV) can be downloaded from the https://bcmi.sjtu.edu.cn/home/seed/index.html

To facilitate data retrieval, the data from the first session of all subjects is utilized in both datasets, the file structure of the datasets should be like:

ExtractedFeatures/
    1/
eeg_feature_smooth/
    1/

Kindly change the file path in the main.py

Run python main.py, and The results will be recorded in TensorBoard. The argument for the dataset_name is set to be seed3 for the SEED dataset, and seed4 for the SEED-IV dataset, respectively.

Run python ablation/witoutMix.py
Run python ablation/withoutNoise.py
Run python ablation/withoutBothMixAndNoise.py

Run python noiseInjectionMethods/maskChannels.py
Run python noiseInjectionMethods/maskTimeSteps.py
Run python noiseInjectionMethods/channelsShuffling.py
Run python noiseInjectionMethods/Dropout.py

Run python T-SNE/generatePlotByTSNE.py

If you found our work useful for your research, please cite our work:

@inproceedings{wang2024dmmr,
  title={DMMR: Cross-Subject Domain Generalization for EEG-Based Emotion Recognition via Denoising Mixed Mutual Reconstruction},
  author={Wang, Yiming and Zhang, Bin and Tang, Yujiao},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={1},
  pages={628--636},
  year={2024}
}

We thank the following repositories for providing helpful functions used in our work: MS-MDA
DANN