xkazm/asfa

Code for ASFA ("Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces")

Code for ASFA

Prerequisites

• python == 3.8.5
• torch == 1.8.1
• numpy == 1.20.1
• scipy == 1.6.1
• mne == 0.22.0
• scikit-learn == 0.23.2
• pyriemann == 0.2.6

Dataset

• Please manually download the datasets BNCI2014001, BNCI2014002, BNCI2014004 by MOABB.

Framework

• bci: common approaches in BCIs:
  • Common spatial pattern (CSP)
  • Euclidean alignment (EA)
  • Minimum distance to mean (MDRM)
  • Riemannian alignment (RA)
  • EEGNet
  • DeepConvNet
• bsfda: black-box source model for source-free domain adaptation:
  • Source: source only
  • Source HypOthesis Transfer (SHOT-IM, SHOT)
  • ASFA, ASFA-aug: our proposed approach, ASFA-aug add data augmentation when performing knowledge distillation
• libs: public function used in this project:
  • augment: augment functions
  • cdan, dan, dann, grl, jan, kernel: files for existing unsupervised domain adaptation approaches, code from https://github.com/thuml/Transfer-Learning-Library
  • dataLoad: load and compute tangent space features for EEG data
  • DataIterator: data iterator when training deep networks
  • network, eegnet, deepconvent, DomainDiscriminator: model definition
  • loss: loss functions
  • utils: common used functions
• sfda: approaches for source-free domain adaptation:
  • Source: source only
  • BAIT
  • Source HypOthesis Transfer (SHOT-IM, SHOT)
  • ASFA: our proposed approach
• uda: approaches for unsupervised domain adaptation:
  • Conditional domain adversarial network (CDAN/CDAN-E)
  • Domain adaptation network (DAN)
  • Domain-adversarial neural network (DANN)
  • Joint adaptation netowrk (JAN)
  • Minimum class confusion (MCC)

Run

When you have prepared the datasets, you can directly run the corresponding .py file.

For example,

cd ASFA
python sfda/ASFA.py --gpu_id '0' --device 'cuda' --fileroot your_data_file_path --output ASFA

Citation

If you find this code useful for your research, please cite our papers

@article{XiaASFA2022,
    title={Privacy-preserving domain adaptation for motor imagery-based brain-computer interfaces},
    author={Kun Xia and Lingfei Deng and Wlodzislaw Duch and Dongrui Wu},
    journal={IEEE Trans. on Biomedical Engineering},
    year={2022},
    vol={69},
    no={11},
    pages={3365-3376}
}

Contact

[email protected]