A Minimal Setup for Spontaneous Smile Quantification Applicable for Valence Detection

Area of Science:

• Applied Neuroscience
• Psychophysiology
• Affective Computing

Background:

• Traditional automatic facial expression tracking shows low validation.
• Reliable measurement of emotional responses is crucial for applied neuroscience.
• Challenges exist in assessing emotions in immersive environments like Virtual Reality.

Purpose of the Study:

• To directly measure facial muscle activity (Zygomaticus Major) during emotional responses.
• To evaluate the relationship between facial muscle activity and subjective emotional ratings (Valence, Liking, Dominance).
• To validate a novel approach for quantifying spontaneous smiles using surface electromyography (sEMG).

Main Methods:

• Utilized single-channel surface electromyography (sEMG) to record Zygomaticus Major muscle activity.
• Participants rated music videos on Valence, Liking, and Dominance.
• Analyzed sEMG data for smile instances (ZygoNum), duration (ZygoLen), and high valence events (ZygoTrace).

Main Results:

• Zygomaticus Major activity (ZygoNum) strongly correlated with Valence ratings.
• Fractal analysis of sEMG confirmed smoother muscle contractions for enjoyment smiles.
• ZygoTrace accurately identified high valence stimuli (76% accuracy) and aligned with EEG-based valence detection.

Conclusions:

• Direct sEMG measurement of the Zygomaticus Major muscle provides a valid method for assessing emotional valence.
• This technique offers a robust alternative to less reliable facial expression analysis methods.
• The approach is suitable for applications requiring accurate emotional assessment, even in challenging conditions like VR.