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

arXiv cs.LG / 5/1/2026

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Key Points

The survey highlights that cross-subject EEG decoding is difficult because large inter-subject variability causes a strong domain shift between training data and unseen test subjects.
It frames cross-subject generalization as a multi-source domain learning problem and proposes subject-independent evaluation protocols to ensure results are valid.
The paper provides a systematic taxonomy of deep learning approaches, grouping them into families such as feature alignment, adversarial learning, feature disentanglement, and contrastive learning.
It concludes with key future directions for robust real-world decoding, including current theoretical limits, how subject identity can be structurally useful, and the potential role of emerging EEG foundation models.

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.