"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
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