The human brain has evolved for group living [1]. Yet we know so little about how it supports dynamic group interactions that the study of real-world social exchanges has been dubbed the "dark matter of social neuroscience" [2]. Recently, various studies have begun to approach this question by comparing brain responses of multiple individuals during a variety of (semi-naturalistic) tasks [3-15]. These experiments reveal how stimulus properties [13], individual differences [14], and contextual factors [15] may underpin similarities and differences in neural activity across people. However, most studies to date suffer from various limitations: they often lack direct face-to-face interaction between participants, are typically limited to dyads, do not investigate social dynamics across time, and, crucially, they rarely study social behavior under naturalistic circumstances. Here we extend such experimentation drastically, beyond dyads and beyond laboratory walls, to identify neural markers of group engagement during dynamic real-world group interactions. We used portable electroencephalogram (EEG) to simultaneously record brain activity from a class of 12 high school students over the course of a semester (11 classes) during regular classroom activities (Figures 1A-1C; Supplemental Experimental Procedures, section S1). A novel analysis technique to assess group-based neural coherence demonstrates that the extent to which brain activity is synchronized across students predicts both student class engagement and social dynamics. This suggests that brain-to-brain synchrony is a possible neural marker for dynamic social interactions, likely driven by shared attention mechanisms. This study validates a promising new method to investigate the neuroscience of group interactions in ecologically natural settings.

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When we feel connected or engaged during social behavior, are our brains in fact "in sync" in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowd-source neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-minute face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine-an artistic Brain-Computer Interface (BCI) installation that translates real-time correlations of each pair's EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain synchrony using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs' trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha power (~7-10 Hz) and beta oscillations (~20-22 Hz). These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowd-sourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, this work is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of "citizen science" research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for this type of out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public ( wp.nyu.edu/mutualwavemachine ) We capitalized on recent real-world social neuroscience research (Dikker et al., 2017;

Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple individuals during social interaction. For example, so-called “hyperscanning” research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.

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