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

TL;DR

This survey reviews deep learning approaches for EEG decoding across subjects, highlighting challenges due to variability, and discusses evaluation protocols, methodologies, and future directions for robust, real-world applications.

Contribution

It provides a comprehensive taxonomy of deep learning methods for cross-subject EEG decoding and formalizes the problem as a multi-source domain issue.

Findings

Systematic taxonomy of current deep learning methods

Formalization of cross-subject EEG decoding as a multi-source domain problem

Discussion of theoretical limitations and future directions

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.