Domain Generalization for Session-Independent Brain-Computer Interface
This research addresses the critical problem of inter/intra-subject variability in EEG for BCI users, aiming to reduce the need for session-specific calibration, which is a major obstacle to practical BCI use.
The paper investigates the zero-calibration problem in Brain-Computer Interfaces (BCI) by evaluating deep learning models and domain generalization (DG) algorithms for session-independent EEG classification. It found that deeper models improved cross-session generalization, but explicit DG algorithms did not outperform empirical risk minimization.
The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to acquire subject/session-specific 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 leave-one-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 subject-specific data, we found that subject-specific data may deteriorate unseen session classification performance due to inter-session variability.