解决脑电图跨站点泛化难题，提升抑郁症诊断模型的可靠性
CRCC: Contrast-Based Robust Cross-Subject and Cross-Site Representation Learning for EEG
- 分离出三种影响泛化的关键偏差因素，通过标准化与约束缓解其影响
- 在严格零样本迁移下，平衡准确率提升10.7个百分点，超越现有方法
- 适合临床脑电分析、跨中心研究及需要高泛化性能的神经解码任务
基于脑电图（EEG）的神经解码模型常因采集站点间的结构化偏差而无法跨站点泛化。我们重新将跨站点临床EEG学习建模为一种偏差分解的泛化问题，其中域偏移来自多个相互作用的来源。我们识别出三个基本偏差因子，并提出一个通用训练框架，通过数据标准化和表征级约束减轻其影响。我们构建了一个针对重度抑郁症的标准化多站点EEG基准数据集，提出CRCC——一种两阶段训练范式，结合编码器-解码器预训练，以及通过跨受试者/站点对比学习和站点对抗优化的联合微调。CRCC在多个场景下持续优于现有最优基线，在严格的零样本站点迁移设置下，平衡准确率提升10.7个百分点，展现出对未见环境的鲁棒泛化能力。
原文摘要 · Abstract (English)
EEG-based neural decoding models often fail to generalize across acquisition sites due to structured, site-dependent biases implicitly exploited during training. We reformulate cross-site clinical EEG learning as a bias-factorized generalization problem, in which domain shifts arise from multiple interacting sources. We identify three fundamental bias factors and propose a general training framework that mitigates their influence through data standardization and representation-level constraints. We construct a standardized multi-site EEG benchmark for Major Depressive Disorder and introduce CRCC, a two-stage training paradigm combining encoder-decoder pretraining with joint fine-tuning via cross-subject/site contrastive learning and site-adversarial optimization. CRCC consistently outperforms state-of-the-art baselines and achieves a 10.7 percentage-point improvement in balanced accuracy under strict zero-shot site transfer, demonstrating robust generalization to unseen environments.
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