Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
4 methodological families to fix brain signal variability—feature alignment to foundation models.
A comprehensive survey published on arXiv by researchers Taida Li, Yujun Yan, Fei Dou, Wenzhan Song, and Xiang Zhang tackles one of the hardest problems in EEG decoding: generalizing models across different subjects. The high inter-subject variability in brain signals leads to severe domain shifts between training and unseen test subjects. The authors formally define cross-subject decoding as a multi-source domain adaptation problem and propose rigorous, subject-independent evaluation protocols to ensure valid performance assessment.
The survey organizes the current literature into four methodological families: feature alignment (matching distributions across subjects), adversarial learning (using domain discriminators), feature disentanglement (separating subject-specific from task-relevant features), and contrastive learning (pulling same-class representations closer). It concludes by examining three critical areas for advancing real-world decoding: theoretical limitations of existing methods, the structural value of subject identity as a conditioning signal, and the emergence of EEG foundation models that could pre-train on diverse populations. This taxonomy provides a clear roadmap for researchers building next-generation brain-computer interfaces.
- Formalizes cross-subject EEG decoding as a multi-source domain shift problem.
- Taxonomy covers feature alignment, adversarial learning, disentanglement, and contrastive learning.
- Highlights EEG foundation models as a promising direction for robust BCIs.
Why It Matters
This survey provides a practical roadmap for building EEG decoders that work reliably across individuals.