This 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.
Keywords: affective computing; data set; electroencephalogram (EEG); emotional corpora; machine learning; modeling human emotion; physiological signals.
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