针对跨数据集脑电情绪识别难题，提出边界感知原型对齐框架，提升模型泛化能力。
Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition
- 以原型引导局部类条件对齐，聚焦类别间差异建模
- 在多个数据集上实现最高6.72%的准确率提升
- 特别适合处理标签噪声多、设备差异大的真实场景
基于脑电（EEG）的情绪识别在跨异构数据集迁移时性能严重下降，源于生理差异、实验范式不同和设备不一致。现有域对抗方法多关注全局边缘对齐，忽略类条件不匹配与决策边界扭曲，限制了跨数据集泛化能力。本文提出统一的原型驱动对抗对齐（PAA）框架，分三阶段构建：PAA-L实现原型引导的局部类条件对齐；PAA-C引入对比语义正则化增强类内紧凑性与类间可分性；PAA-M为完整边界感知配置，采用双关系感知分类器与三阶段对抗优化，显式修正决策边界附近的争议样本。通过原型引导子域对齐、对比判别增强与边界感知聚合，在统一对抗架构中将情绪识别重构为关系驱动的表征学习问题，降低对标签噪声敏感性，提升跨域稳定性。在SEED、SEED-IV、SEED-V数据集上，四种跨数据集评估协议下平均提升分别为6.72%、5.59%、6.69%、4.83%。该框架还有效推广至临床抑郁症识别场景，验证其在真实异构环境中的鲁棒性。源码见：https://github.com/WuCB-BCI/PAA
原文摘要 · Abstract (English)
Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class-conditional mismatch and decision boundary distortion, limiting cross-corpus generalization. In this work, we propose a unified Prototype-driven Adversarial Alignment (PAA) framework for cross-corpus EEG emotion recognition. The framework is progressively instantiated in three configurations: PAA-L, which performs prototype-guided local class-conditional alignment; PAA-C, which further incorporates contrastive semantic regularization to enhance intra-class compactness and inter-class separability; and PAA-M, the full boundary-aware configuration that integrates dual relation-aware classifiers within a three-stage adversarial optimization scheme to explicitly refine controversial samples near decision boundaries. By combining prototype-guided subdomain alignment, contrastive discriminative enhancement, and boundary-aware aggregation within a coherent adversarial architecture, the proposed framework reformulates emotion recognition as a relation-driven representation learning problem, reducing sensitivity to label noise and improving cross-domain stability. Extensive experiments on SEED, SEED-IV, and SEED-V demonstrate state-of-the-art performance under four cross-corpus evaluation protocols, with average improvements of 6.72\%, 5.59\%, 6.69\%, and 4.83\%, respectively. Furthermore, the proposed framework generalizes effectively to clinical depression identification scenarios, validating its robustness in real-world heterogeneous settings. The source code is available at \textit{https://github.com/WuCB-BCI/PAA}
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