# Reviewing digital collaborative interactions with multimodal hyperscanning through an ever-growing database

## TL;DR

This paper reviews how digital collaboration affects human interaction using mobile hyperscanning and highlights the need for better methods and standards in the field.

## Contribution

The paper introduces a comprehensive review and database (InterBrainDB) to address fragmentation in hyperscanning research on digital collaboration.

## Key findings

- Most studies use unimodal neuroimaging like EEG or fNIRS, with limited multimodal approaches.
- Cooperative tasks and same-sex dyads are common, while non-verbal interactions are studied more than verbal ones.
- Functional connectivity is widely used, but effective connectivity and machine learning are rarely applied.

## Abstract

Digital technologies now mediate a substantial proportion of human collaboration, reshaping how individuals coordinate attention, share information, and jointly act on goals. These digitally mediated interactions engage neural, physiological, and behavioral processes differently compared to face-to-face settings. Mobile hyperscanning, i.e., simultaneous (neuro-)physiological measures of two or more individuals, offers a unique window into these multidimensional dynamics. Yet, the existing literature is highly fragmented in design, modality, and analytic rigor, making it difficult to accumulate knowledge. This review systematically synthesizes hyperscanning research investigating collaboration involving digital components and identifies key methodological and conceptual gaps that must be addressed to advance the field.

We searched Scopus, PubMed, and Web of Science (April 2025) for mobile hyperscanning studies on digital collaboration. Forty-five eligible studies involving simultaneous measurements of at least two healthy adults engaged in collaborative tasks with a digital interaction component were included. Studies were categorized across 13 dimensions, including modality, task design, interaction type, analysis method, and cognitive domain. To ensure transparency and support cumulative synthesis, we created a continuously updated online resource (“InterBrainDB”).

Most studies relied on unimodal neuroimaging, predominantly electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS), with only seven studies implementing multimodal combinations. Study designs favored cooperative tasks or naturalistic scenarios with symmetrical roles, typically using same-sex dyads of unfamiliar individuals. Non-verbal interaction was studied slightly more often than verbal. Analytically, functional connectivity dominated, whereas effective connectivity, multimodal fusion, and machine learning were scarcely used. Executive and social cognition were more frequently investigated than creativity, memory, and language.

Research on digital collaboration through hyperscanning is growing, yet progress is limited by methodological heterogeneity, narrow use of modalities, and analytical conservatism. Future advances will require: (1) multimodal integration to fully capture neural, physiological, and behavioral dynamics; (2) systematic comparisons across varying degrees of digitalization to understand how technology shapes interaction; (3) physiology-informed analysis frameworks capable of modeling high-dimensional interpersonal dynamics; and (4) clearer reporting standards to enable reproducibility and large-scale synthesis. Resources like our InterBrainDB can structure a community-driven progress toward ecologically grounded models of digitally mediated collaboration, a domain of increasing scientific and societal relevance.