Understanding the role of emotion in decision making process: using machine learning to analyze physiological responses to visual, auditory, and combined stimulation

This study investigates autonomic and central nervous system responses to emotional stimuli, including images, auditory cues, and their combination while recording physiological signals, namely the electrocardiogram, blood volume pulse, galvanic skin response, pupillometry, respiration, and the electroencephalogram. The primary goal of the proposed analysis is to compare emotional stimulation methods and to identify the most effective approach for distinct physiological patterns. A novel feature selection technique is applied to further optimize the separation of four emotional states. Basic machine learning approaches are used in order to discern emotions as elicited by different kinds of stimulation. Electroencephalographic signals, Galvanic skin response and cardio-respiratory coupling-derived features provided the most significant features in distinguishing the four emotional states. Further findings highlight how auditory stimuli play a crucial role in creating distinct physiological patterns that enhance classification within a four-class problem. When combining all three types of stimulation, a validation accuracy of 49% was achieved. The sound-only and the image-only phases resulted in 52% and 44% accuracy respectively, whereas the combined stimulation of images and sounds led to 51% accuracy. Isolated visual stimuli yield less distinct patterns, necessitating more signals for relatively inferior performance compared to other types of stimuli. This surprising signific...
Source: Frontiers in Human Neuroscience - Category: Neuroscience Source Type: research