Neural Mechanisms of Social Emotion Perception: An EEG Hyper-Scanning Study

Abstract

EEG-based hyper-scanning refers to two or more subjects engaged in a task together or performing the same action together while neurophysiological signals are simultaneously recorded from them. This is one of the manners for investigating between-subject neural activities involved in social interactions. Emotion perception plays an important role in human social interactions. Interaction and emotional state influence each other. In this study, we aim to investigate how between-subject interaction modulates emotion perception based on event related potentials (ERPs), connectivity analysis and classification analysis. We found that there are distinct differences appearing between paired subjects who performed the task together, which are early ERP components (N250 and N400), late ERP components (P1500 and N1500), and the greater amplitude in N250 for the seconding responding subject compared to the first one. In the exploration of connectivity using phase locking value (PLV), we found that there are significant differences among different frequency bands for each subject under positive and negative stimuli and the significant difference of hyper-connectivity existed in the gamma frequency band between positive and negative stimulus trials. In the classification analysis, we compared the hyper-features for

EEG Functional Connectivity Underlying Emotional Valance and Arousal Using Minimum Spanning Trees

“…When studying emotional facial expression, Li found that the coherence of negative emotion was greater than that of positive emotion in both the low and high γ bands (Li et al, 2015). Zhu L found that the phase lock value of positive video stimulation was significantly lower than that of negative stimulation in the β and γ bands (Zhu et al, 2018).…”

“…At present, traditional EEG analysis methods, including methods that focus on the power spectrum (Liu et al, 2018), coherence (Zhang et al, 2012;Mu et al, 2017) and phase lock value (Zhu et al, 2018), are widely used. The brain regions coordinate and cooperate with each other to form a complex brain network, and the development of graph theory provides a perfect tool for brain network analysis (Rubinov and Sporns, 2010;Calhoun et al, 2018).…”

Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements

“…However, in the future, we will also explore an alternative study protocol where dyads will go through multiple artificially induced conversation scenarios (e.g., told to argue with each other), and classification methods will be used to assign physiological data to one of the possible scenarios. While less natural than the current protocol, this is likely to provide more balanced data, and classification algorithms are more common than regression algorithms in both studies of physiological synchrony (Hernandez et al, 2014;Konvalinka et al, 2014;Muszynski et al, 2018;Zhu et al, 2018;Brouwer et al, 2019;Verdiere et al, 2019;Darzi and Novak, 2021) and general affective computing (Novak et al, 2012;Aranha et al, 2019). Unrelated to the above classification approach, we may also consider a multi-day protocol where engagement estimation algorithms are trained on data from one session, then tested on data from another session.…”

“…However, while there have been many studies targeting group-level analysis of physiological synchrony (e.g., correlating synchrony and engagement in a large sample), there has been relatively little work on quantifying engagement or other interpersonal states at the level of individual dyads (e.g., tracking interpersonal engagement of a specific dyad over time). A handful of studies have used classification algorithms with a single physiological modality (e.g., electroencephalography alone) to discriminate between two states (e.g., engaged vs. unengaged dyads) ( Hernandez et al, 2014 ; Konvalinka et al, 2014 ; Muszynski et al, 2018 ; Zhu et al, 2018 ; Brouwer et al, 2019 ; Pan et al, 2020 ) with one study discriminating between four affective states ( Verdiere et al, 2019 ). A final study used regression algorithms to map physiological synchrony to self-reported arousal and valence on 1–9 scales using electroencephalography during video watching ( Ding et al, 2021 ).…”

Thoughts on neurophysiological signal analysis and classification

“…2(B)]. In a hyperscanning study of emotion perception, significantly different connections between positive emotion and negative emotion were observed not only within each brain but also between the brains [36]. Currently, the amount of studies investigating between-brain connectivity is very limited.…”