基于机器学习的可解释建模用于主观情感动态传感,使用面部EMG
Naoya Kawamura1,2, Wataru Sato1,2, Koh Shimokawa2
1Computational Cognitive Neuroscience Laboratory, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8501, Japan.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
这项研究表明,与线性模型相比,非线性机器学习模型更好地捕捉主观情感价值和面部电肌图 (EMG) 信号之间的复杂关系. 这些发现提高了使用面部EMG的情感感知能力.
科学领域:
- 心理生理学 心理生理学
- 情感神经科学是一种神经科学.
- 机器学习在心理学中的应用
背景情况:
- 了解主观情绪体验与生理信号之间的联系至关重要.
- 以前的研究表明,情绪价值动态和面部电肌图 (EMG) 之间存在线性关联.
- 情绪价值和面部EMG之间的非线性关系的潜力仍然未被探索.
研究的目的:
- 调查动态主观情感价值和面部EMG之间的非线性关联.
- 为了比较非线性机器学习模型与情感感知线性回归的性能.
- 探索生理信号和情绪体验之间的复杂相互作用.
主要方法:
- 重新分析了50名参与者观看情感电影片段的现有数据.
- 测量波纹超肌和肌主要肌肉的动态价值评级和面部EMG.
- 应用多线性回归,随机森林和长短期记忆 (LSTM) 机器学习模型.
主要成果:
- 非线性机器学习模型 (随机森林,LSTM) 在交叉验证中显著优于线性回归.
- 沙普利添加剂扩展揭示了EMG特征和价值动态之间的非线性和交互性关联.
- 面部EMG表现出一种复杂的,与主观情感价值动态的非线性关系.
结论:
- 与线性方法相比,非线性机器学习模型更准确地适应情绪动态.
- 这项研究推进了通过面部EMG感知情绪.
- 这项研究加深了我们对情绪复杂的主观-生理学关联的理解.
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