# Domain Generalization for Session-Independent Brain-Computer Interface

arXiv: https://arxiv.org/abs/2012.03533 · HTML: https://mlsanity.com/paper/2012.03533/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization through Meta-Learning: A Survey](https://mlsanity.com/paper/2404.02785/) (2024)
- [Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation](https://mlsanity.com/paper/2212.13887/) (2022)
- [Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface](https://mlsanity.com/paper/2112.09243/) (2021)
- [Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts](https://mlsanity.com/paper/2304.09050/) (2023)
- [Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization](https://mlsanity.com/paper/2312.09461/) (2023)

## Abstract (arXiv)

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.