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2021 9th International Winter Conference on Brain-Computer Interface (BCI) by Han, Dong-Kyun (author);Jeong, Ji-Hoon (author) is a nonfiction available to read on EtoBox.

What is 2021 9th International Winter Conference on Brain-Computer Interface (BCI) about?

The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the braincomputer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to acquire subject/sessionspecific data to tune the model every time the system is used. This problem is recognized as a major obstacle to BCI, and to overcome it, an approach based on domain generalization (DG) has recently emerged. The main purpose of this paper is to reconsider how the zero-calibration problem of BCI for a realistic situation can be overcome from the perspective of DG tasks. In terms of the realistic situation, we have focused on creating an EEG classification framework that can be applied directly in unseen sessions, using only multi-subject/-session data acquired previously. Therefore, in this paper, we tested four deep learning models and four DG algorithms through leaveone-session-out validation. Our experiment showed that deeper and larger models were effective in cross-session generalization performance. Furthermore, we found that none of the explicit DG algorithms outperformed empirical risk minimization. Finally, by comparing the results of fine-tuning using

Who reads 2021 9th International Winter Conference on Brain-Computer Interface (BCI)?

Author

Han, Dong-Kyun (author);Jeong, Ji-Hoon (author)

Publisher

IEEE

Published

2021

Language

EN

Category

nonfiction