用几何机器学习分析脑电数据，客观识别压力状态
Decoding the Stressed Brain with Geometric Machine Learning
- 构建结合脑区位置与信号关联的图结构，输入时空图卷积网络
- 在SAM-40数据集上分类准确率优于传统模型，关键通道与区域可解释
- 适合神经科学、心理健康与可穿戴设备研究者参考
压力显著影响身心疾病，但传统自评量表主观性强。本研究提出一种新型几何机器学习框架，通过原始脑电（EEG）信号检测压力。方法将电极空间位置构成的结构连接性与成对信号相关性形成的功能连接性融合为图结构，再由时空图卷积网络（ST-GCN）捕捉时空动态。在SAM-40数据集上的实验表明，该模型在所有关键分类指标上均优于标准机器学习模型，并通过消融分析揭示了关键电极与脑区，提升了可解释性。结果为更客观、精准的压力检测提供了新路径。
原文摘要 · Abstract (English)
Stress significantly contributes to both mental and physical disorders, yet traditional self-reported questionnaires are inherently subjective. In this study, we introduce a novel framework that employs geometric machine learning to detect stress from raw EEG recordings. Our approach constructs graphs by integrating structural connectivity (derived from electrode spatial arrangement) with functional connectivity from pairwise signal correlations. A spatio-temporal graph convolutional network (ST-GCN) processes these graphs to capture spatial and temporal dynamics. Experiments on the SAM-40 dataset show that the ST-GCN outperforms standard machine learning models on all key classification metrics and enhances interpretability, explored through ablation analyses of key channels and brain regions. These results pave the way for more objective and accurate stress detection methods.
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