Kawamura Naoya, Sato Wataru, Shimokawa Koh, Fujita Tomohiro, Kawanishi Yasutomo
Computational Cognitive Neuroscience Laboratory, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8501, Japan.
Psychological Process Team, Guardian Robot Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan.
Sensors (Basel). 2024 Feb 27;24(5):1536. doi: 10.3390/s24051536.
Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective-physiological association.
理解主观情绪体验与生理信号之间的关联具有实践和理论意义。先前的心理生理学研究表明，动态情绪效价体验与面部肌电图（EMG）活动之间存在线性关系。然而，主观情绪效价动态是否以及如何与面部EMG变化呈非线性关系仍不清楚。为了研究这个问题，我们重新分析了之前两项研究的数据，这两项研究测量了观看情感电影片段的50名参与者的动态效价评分以及皱眉肌和颧大肌的面部EMG。我们采用了多线性回归分析和两种非线性机器学习（ML）模型：随机森林和长短期记忆。在交叉验证中，这些ML模型在均方误差和相关系数方面优于线性回归。使用SHapley加性解释工具对随机森林模型进行解释，揭示了几个EMG特征与主观效价动态之间的非线性和交互关联。这些发现表明，非线性ML模型比传统线性模型能更好地拟合主观情绪效价动态与面部EMG之间的关系，并突出了一种非线性和复杂的关系。这些发现鼓励使用面部EMG进行情绪感知，并为主体-生理关联提供了见解。
