Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

2026-04-29

AI Summary

This study addresses the challenge of cross-subject generalization in electroencephalography (EEG) decoding, which is hindered by high inter-subject variability. The problem is formalized as a multi-source domain generalization task, and a rigorous subject-independent evaluation protocol is introduced. The work establishes the first systematic taxonomy of deep learning approaches for cross-subject EEG decoding, categorizing existing methods into four paradigms: feature alignment, adversarial learning, feature disentanglement, and contrastive learning. Furthermore, it clarifies key limitations of current approaches concerning theoretical foundations, utilization of subject identity information, and underexplored potential of foundational models. The paper concludes with a forward-looking discussion on pathways toward EEG foundation models, offering a clear roadmap for developing robust and practical EEG decoding systems.

Abstract

Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.

Problem

• cross-subject generalization
• EEG decoding
• domain shift
• inter-subject variability
• deep learning

Innovation

• cross-subject generalization
• domain shift
• feature disentanglement
• adversarial learning
• EEG foundation models