Emotional arousal pattern (EMAP): A new database for modeling momentary subjective and psychophysiological responding to affective stimuli

Abstract

AbstractThis article describes a new database (named “EMAP”) of 145 individuals' reactions to emotion‐provoking film clips. It includes electroencephalographic and peripheral physiological data as well as moment‐by‐moment ratings for emotional arousal in addition to overall and categorical ratings. The resulting variation in continuous ratings reflects inter‐individual variability in emotional responding. To make use of the moment‐by‐moment data for ratings as well as neurophysiological activity, we used a machine learning approach. The results show that algorithms that are based on temporal information improve predictions compared to algorithms without a temporal component, both within and across participant modeling. Although predicting moment‐by‐moment changes in emotional experiences by analyzing neurophysiological activity was more difficult than using aggregated experience ratings, selecting a subset of predictors improved the prediction. This also showed that not only single features, for example, skin conductance, but a range of neurophysiological parameters explain variation in subjective fluctuations of subjective experience.