Complexity-Based Coupling of Cardiac and Facial Muscle Responses to Olfactory Stimuli

Abstract

This study examines the dynamic relationship between cardiac activity and facial muscle responses to olfactory stimuli of varying complexity, employing nonlinear analytical techniques. Electrocardiography (ECG) and facial electromyography (EMG) signals were recorded from healthy participants during rest and while they sniffed four different odors with varying molecular complexities, including Pineapple, Banana, Vanilla, and lemon flavors. Fractal dimension, sample entropy, and approximate entropy were computed for the R-R interval time series (derived from ECG signals) and for the EMG signals. The results indicate an increase in physiological signal complexity with the rise in molecular complexity of odors. Additionally, strong correlations were observed between the complexity metrics of ECG and EMG signals, suggesting coordinated autonomic and somatic responses. These findings underscore the importance of complexity-based analysis in comprehending the integrated physiological responses to emotionally and cognitively engaging stimuli.