"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
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