Emotion is a constructed phenomenon that emerges from the dynamic interaction of multiple components neurologically, physiologically and behaviorally. Such dynamics can not be captured by static and controlled experiments. Hence, the study of emotion with a naturalistic paradigm is needed. In this dataset, multimedia naturalistic stimuli are used to acquire the emotional dynamics using EEG, ECG, EMG and behavioural scales. The stimuli are multimedia videos collected from youtube for 372 affective words, analyzed with multimedia content analysis to filter out non-emotional stimuli and then validated with university students. The validated stimuli had the least variance in subjective ratings on self-assessment scales. The stimuli are then used to acquire neurological dynamics along with peripheral channels and subjective ratings-valence, arousal, dominance, liking, familiarity, relevance and emotion category. Both the raw data and pre-processed data is provided along with the pre-processing pipeline. This data can be utilized to study dynamic activation and connectivity in the whole brain source localization study, understand the mutual interaction between the central and autonomic nervous system, understand temporal hierarchy using multiresolution tools, and perform machine learning-based classification and complex networks analysis. The data is accessible at \url{10.18112/openneuro.ds003751.v1.0.0}
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 mechanisms 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. The channels with high predictability were source localized to the brain regions in DMN, sensorimotor and salience networks. Hence, in this naturalistic study, we find that the domain-general systems contribute to emotion construction.
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