Naturalistic paradigm reveals multi-component emotion dynamics in theta and beta bands using DENS dataset

Abstract

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.

The degree of context un/familiarity impacts the emotional feeling and preaware cardiac-brain activity: a study with emotionally salient naturalistic paradigm using DENS Dataset

“…The grouping of emotions is described in [35] and supplementary fig- 1. The subject has rated their familiarity in the familiarity scale ranging from 1(low-familiarity:LF) to 5(high-familiarity:HF) for each emotional stimuli.…”

“…It provides evidence for the association of body physiology with the conceptual hubs and VMA regions. We believe that during high-context familiarity, the already learnt emotion concept is reactivated and the associated representations, including visceromotor representation is simulated to re-experience the feeling of an emotion which is felt in the past with a similar context or event [35]. The learning of emotions with the autonomic coregulation of visceromotor activity starts early in childhood during mother-infant interaction.…”

“…Mostly, emotion research is done in the lab setting with experimental paradigm lacking ecological validity. Recently, a shift in emotion research using naturalistic paradigm is adapted to probe the cortical dynamics [38,34,35]. The stimuli in naturalistic paradigm includes virtual reality immersive experience [25], emotional and neutral movie clips [55,34], and emotionally salient films [20].…”

“…The stimuli in naturalistic paradigm includes virtual reality immersive experience [25], emotional and neutral movie clips [55,34], and emotionally salient films [20]. The naturalistic paradigm offers the probe to dynamically track the taskrelated neural responses, autonomic responses and subjective feedback in a more real, dynamic and natural environment [35]. In emotion research, using a naturalistic paradigm can extend the understanding beyond what is contributed by experiments in the literature with controlled experimental settings (experiments with artificial stimuli lacking ecological validity).…”

Development of an automated online proctoring system

“…And at the same time, in order to reduce the load on the system and save resources, they do not analyze the video in detail, but rather a set of frames every few seconds or even minutes. These strict requirements and the poor implementation of video sequence analysis (per-frame analysis does not give the full information about the emotions of a person in comparison to spatio-temporal analysis [17,18], [19]) result in systems that are unable to adequately assess student behavior: they either miss violations of exam rules or react incorrectly to a student's blinking or yawning.…”