用图正则化提升脑电情绪识别跨会话稳定性
EEG-based Graph-guided Domain Adaptation for Robust Cross-Session Emotion Recognition
- 通过联合对齐全局与类别分布，保留脑电数据结构
- 在SEED-IV数据集上跨会话准确率超80%
- 发现γ频段和中央顶叶、额前区对情绪识别最关键
准确识别人类情绪状态对于人机交互至关重要。脑电图（EEG）因具备高时间分辨率并直接反映神经活动，是情绪识别的可靠来源。然而，不同记录会话间的差异严重影响模型泛化能力。为此，我们提出EGDA框架，通过联合对齐全局（边缘）和类别特定（条件）分布来减少跨会话差异，同时利用图正则化保持EEG数据内在结构。在SEED-IV数据集上的实验表明，EGDA在三个迁移任务中分别获得81.22%、80.15%和83.27%的准确率，优于多个基线方法。此外分析指出，γ频段最具判别性，中央-顶叶和额前脑区对可靠情绪识别尤为关键。
原文摘要 · Abstract (English)
Accurate recognition of human emotional states is critical for effective human-machine interaction. Electroencephalography (EEG) offers a reliable source for emotion recognition due to its high temporal resolution and its direct reflection of neural activity. Nevertheless, variations across recording sessions present a major challenge for model generalization. To address this issue, we propose EGDA, a framework that reduces cross-session discrepancies by jointly aligning the global (marginal) and class-specific (conditional) distributions, while preserving the intrinsic structure of EEG data through graph regularization. Experimental results on the SEED-IV dataset demonstrate that EGDA achieves robust cross-session performance, obtaining accuracies of 81.22%, 80.15%, and 83.27% across three transfer tasks, and surpassing several baseline methods. Furthermore, the analysis highlights the Gamma frequency band as the most discriminative and identifies the central-parietal and prefrontal brain regions as critical for reliable emotion recognition.
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