解决脑电跨被试识别难题，梳理深度学习关键方法。
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
- 将跨被试脑电解码归为多源域问题，构建标准化评估框架。
- 系统分类现有方法：特征对齐、对抗学习、特征解耦与对比学习。
- 指出当前局限，强调被试身份结构价值及脑电基础模型潜力。
基于深度学习的跨被试脑电解码受高个体差异影响，导致训练与未见被试间存在严重领域偏移。本文综述了专为应对该跨被试泛化挑战设计的深度学习方法。通过将跨被试场景形式化为多源域问题，提出严格且独立于被试的评估协议以确保有效性。核心部分是对现有文献的系统分类，划分为特征对齐、对抗学习、特征解耦和对比学习四类方法。最后，探讨推动鲁棒真实应用解码的三个关键要素：当前方法的理论局限、被试身份的结构价值，以及脑电基础模型的兴起。
原文摘要 · Abstract (English)
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.
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