"We propose augmentation-based source-free adaptation (ASFA), which consists of two parts: 1) source model training, where a novel data augmentation approach is proposed for MI EEG signals to improve the cross-subject generalization performance of the source model; and, 2) target model training, which simultaneously considers uncertainty reduction for domain adaptation and consistency regularization for robustness. ASFA only needs access to the source model parameters, instead of the raw EEG data, thus protecting the privacy of the source domain. ... This is the first work on completely source-free domain adaptation for EEG-based BCIs."

K. Xia, L. Deng, W. Duch and D. Wu. Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces. IEEE Transactions on Biomedical Engineering. 19 April 2022. https://doi.org/10.1109/TBME.2022.3168570

#methods #noninvasive_BCIs #BCI_classifiers #domain_adaptation

"we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor."

Musellim S, Han DK, Jeong JH, Lee SW. Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces. arXiv preprint arXiv:2204.07358. 2022 Apr 15. https://doi.org/10.48550/arXiv.2204.07358

#methods #noninvasive_BCIs #BCI_classifiers #domain_adaptation #BCI_papers