脑电解码的跨学科泛化：深度学习方法综述

摘要(中文译文)

深度学习在跨被试脑电图（EEG）解码中受到被试间高变异性的阻碍，这在训练被试与未见测试被试之间引入了严重的域偏移。本综述对专门为解决这一跨被试泛化挑战而设计的深度学习方法进行了全面回顾。为奠定分析基础，我们将跨被试设定形式化为多源域问题，并阐明了有效评估所需的严格、被试独立的评估方案。本综述的核心是将当前文献系统性地分类为若干离散的方法学家族，包括特征对齐、对抗学习、特征解耦和对比学习。最后，我们考察了推进稳健、真实世界解码的三个关键要素：当前方法的理论局限性、被试身份的结构性价值，以及EEG基础模型的兴起。

Abstract

Deep learning for cross-subject electroencephalography (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.

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