Analyzing Emotional Dynamics: Transition Network Insights from Electrodermal Activity.

Area of Science:

• Neuroscience
• Psychophysiology
• Data Science

Background:

• Emotions are complex processes involving cognitive, affective, physiological, and expressive elements.
• Electrodermal Activity (EDA) measures skin's electrical properties, revealing nonlinear and non-stationary signal characteristics.
• Objective analysis of emotional states is crucial for clinical applications.

Purpose of the Study:

• To apply a transition network approach for analyzing emotional states using EDA signals.
• To investigate the efficacy of this method in differentiating emotional dimensions, particularly arousal.
• To explore the clinical relevance of EDA signal analysis for emotional states.

Main Methods:

• EDA signals were obtained from a public dataset and decomposed into phasic and tonic components.
• The phasic component was transformed into a binary sequence for transition network analysis.
• Twelve symbolic features were extracted, and statistical significance (p < 0.05) was assessed.

Main Results:

• The transition network approach successfully differentiated emotional states based on EDA signals.
• Word probability, Renyi entropy, and frequent pattern symbols significantly varied in differentiating the arousal dimension.
• The findings indicate the method's capability to distinguish emotional arousal levels.

Conclusions:

• The proposed transition network method offers a novel approach to analyze emotional states via EDA signals.
• This technique shows promise for objective assessment in clinical settings, aiding diagnosis and treatment monitoring.
• The findings contribute to understanding the bio-behavioral mechanisms underlying emotional arousal and valence.