筛选出两种可重复的皮肤电反应特征，精准关联情绪唤醒度。
Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
- 用控制错误发现率的T-Rex方法系统筛选生理特征
- 仅2个皮肤电特征在30人数据中100%重现显著相关性
- 为心理疾病识别等高安全场景提供可信生理模型
在情感计算中，如何可靠地将主观情绪体验与客观生理指标关联是关键挑战。本初步研究通过分析心血管和皮肤电信号中的生理特征，探究其与连续自评唤醒水平的关联。基于持续标注情绪信号数据集（CAMELEON），对30名参与者观看短时情绪诱发视频时的164个生理特征进行分析。采用终止随机实验（T-Rex）方法，在用户设定的错误发现率控制下进行变量选择。结果表明，在所有候选特征中，仅有两个皮肤电衍生特征表现出可重复且统计显著的唤醒度关联，确认率达100%。该结果凸显了在生理特征选择中严格可重复性评估的重要性，这一环节常被情感计算领域忽视。本方法对精神障碍识别、人机交互等需高可信白盒模型的安全关键场景具有重要应用前景。
原文摘要 · Abstract (English)
In Affective Computing, a key challenge lies in reliably linking subjective emotional experiences with objective physiological markers. This preliminary study addresses the issue of reproducibility by identifying physiological features from cardiovascular and electrodermal signals that are associated with continuous self-reports of arousal levels. Using the Continuously Annotated Signal of Emotion dataset, we analyzed 164 features extracted from cardiac and electrodermal signals of 30 participants exposed to short emotion-evoking videos. Feature selection was performed using the Terminating-Random Experiments (T-Rex) method, which performs variable selection systematically controlling a user-defined target False Discovery Rate. Remarkably, among all candidate features, only two electrodermal-derived features exhibited reproducible and statistically significant associations with arousal, achieving a 100\% confirmation rate. These results highlight the necessity of rigorous reproducibility assessments in physiological features selection, an aspect often overlooked in Affective Computing. Our approach is particularly promising for applications in safety-critical environments requiring trustworthy and reliable white box models, such as mental disorder recognition and human-robot interaction systems.
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