Manuscript Topics

Hyperscanning refers to the simultaneous acquisition of neural and physiological data from more than one user enabling the possibility to study how they interact in more or less naturalistic contexts. Biosignals synchronization between people engaged in social interactions can be observed in heart rate variability, respiratory rate, and skin conductance, as well as in the hemodynamic and oscillatory activity of the brain. In particular, measures of inter-brain synchrony typically quantify similarities in the time series extracted from multiple users using neuroimaging techniques, such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS).

The interest in the so-called “social brain” has been rapidly growing, although it remains one of the most challenging issues in the current neuroscience scenario. Recent studies demonstrated how a deeper understanding of the neurophysiological basis of social behavior can be gained by analyzing two or more interacting people as a unique system. Such an approach, where neural activity from two or more people can be modeled together, has been called ‘two(multi)-person neuroscience’, and several techniques developed in this context allowed for the evaluation of brain-to-brain coupling/concordance/connectivity. Despite its relatively short history, hyperscanning and two-person neuroscience have been applied to many successful applications including motor synchronization, music production, verbal and non-verbal communication, shared attention, decision-making, classroom learning, and cooperative piloting.

However, hyperscanning-based algorithms and analysis pipelines still present many open issues and require further investigation. The rapidly-increasing number of published hyperscanning studies highlights the importance of (i) addressing methodological pitfalls in order to improve the reliability of future hyperscanning applications (ii) identifying the questions that existing studies have already answered as common ground for future studies.

This Special Issue aims at collecting the latest works involving the development of methods for multi–brain connectivity estimation under different experimental conditions. Areas of interest covered in this section include:
• Multivariate Information measures (time and frequency domains)
• Granger Causality-based connectivity methods
• Information Dynamics
• Brain network analysis (Graph Theory, Community Detection)
• Human Performance Envelope (HPE)
• Human Machine Interactions (HMI)
• Human Cobots Interactions

All manuscript types are considered, including original basic science reports, translational research, clinical studies, review articles, and methodology papers.

Keywords: Hyperscanning, Brain-to-brain Connectivity, Multi-brain Network, Social Science, Artificial Intelligence, Neuroimaging, Neuroscience, Neurophysiological Measures, Multimodal Approach

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