  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.
