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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG.

Naoya Kawamura1,2, Wataru Sato1,2, Koh Shimokawa2

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Summary
This summary is machine-generated.

This study reveals that nonlinear machine learning models better capture the complex relationship between subjective emotional valence and facial electromyography (EMG) signals compared to linear models. These findings enhance emotion sensing capabilities using facial EMG.

Keywords:
SHapley Additive exPlanation (SHAP)facial electromyography (EMG)long short-term memory (LSTM)random forest regressionvalence

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Area of Science:

  • Psychophysiology
  • Affective Neuroscience
  • Machine Learning in Psychology

Background:

  • Understanding the link between subjective emotional experiences and physiological signals is crucial.
  • Previous research indicated a linear association between emotional valence dynamics and facial electromyography (EMG).
  • The potential for nonlinear relationships between emotional valence and facial EMG remained unexplored.

Purpose of the Study:

  • To investigate nonlinear associations between dynamic subjective emotional valence and facial EMG.
  • To compare the performance of nonlinear machine learning models against linear regression for emotion sensing.
  • To explore the complex interplay between physiological signals and emotional experiences.

Main Methods:

  • Re-analysis of existing data from 50 participants viewing emotional film clips.
  • Measurement of dynamic valence ratings and facial EMG from corrugator supercilii and zygomatic major muscles.
  • Application of multilinear regression, random forest, and long short-term memory (LSTM) machine learning models.

Main Results:

  • Nonlinear machine learning models (random forest, LSTM) significantly outperformed linear regression in cross-validation.
  • SHapley Additive exPlanation revealed nonlinear and interactive associations between EMG features and valence dynamics.
  • Facial EMG exhibits a complex, nonlinear relationship with subjective emotional valence dynamics.

Conclusions:

  • Nonlinear machine learning models provide a more accurate fit for emotion dynamics compared to linear approaches.
  • This research advances emotion sensing through facial EMG.
  • The study deepens our understanding of the intricate subjective-physiological association in emotions.