INDIVIDUAL EMOTION RECOGNITION AND SUBGROUP ANALYSIS FROM PSYCHOPHYSIOLOGICAL SIGNALS

Abstract

This study involves intra-and inter-individual emotion classifications from psychophysiological signals and subgroup analysis on the influence of gender and age and their interaction on the emotion recognition. Individual classifications are conducted using a selection of feature optimization, classification and evaluation approaches. The subgroup analysis is based on the inter-individual classification. Emotion elicitation is conducted using standardized pictures in the Valence-Arousal-Dominance dimensions and affective states are classified into five different category classes. Advantageous intra-individual rates are obtained via multi-channel classification and the respiration best contributes to the recognition. High inter-individual variances are obtained showing large variability in physiological responses between the subjects. Classification rates are significantly higher for women than for men for the 3-category-class of Valence. Compared to old subjects, young subjects have significantly higher rates for the 3-category-class and 2-category-class of Dominance. Moreover, young men's classification performed the best among the other subgroups for the 5-category-class of Valence/Arousal.