An article by Dr. Kawamura et al. has been published in Sensors

An article by Drs. Kawamura, Sato, Shimokawa (Psychological Process Research Team), Fujita, and Kawanishi (Multimodal Data Recognition research team) has been published in Sensors.
This study shows that nonlinear machine learning models can better fit the relationship between subjective emotional valence dynamics and facial electromyography activities than conventional linear models and can offer insight into the subjective–physiological association.
Details are as follows.

Kawamura, N., Sato, W., Shimokawa, K., Fujita, T., & Kawanishi, Y. (2024).
Machine learning-based interpretable modeling for subjective emotional dynamics sensing using facial EMG.
Sensors, 24, 1536.