A study of spatio-temporal dynamics of emotion processing usingDENS dataset

The emotion research with artificial stimuli does not represent the dynamic processing of emotions in real-life situations. The lack of data on emotion with the ecologically valid naturalistic paradigm hinders the knowledge of emotion mechanism in a real-world interaction. To this aim, we collected the emotional multimedia clips, validated them with the university students, recorded the neuro-physiological activities and self-assessment ratings for these stimuli. Participants localized their emotional feelings (in time) and were free to choose the best emotion for describing their feelings with minimum distractions and cognitive load. The obtained electrophysiological and self-assessment responses were analyzed with functional connectivity, machine learning and source localization techniques. We observed that the connectivity patterns in the theta and beta band could differentiate emotions better. Using machine learning, we observed that the classification of affective self-assessment features, namely dominance, familiarity, and self-relevance, involves midline brain regions responsible for mentalization and event construction activity compared to valence and arousal, which were mainly associated with lateral brain regions. This finding advocates the need for more than two dimensions for emotion representation. In addition, the channels with high predictability were source localized to the brain regions in default-mode, sensorimotor and salience networks. Hence, in this naturalistic study, we find that the domain-general systems contribute to emotion construction.