Hyper-brain independent component analysis (HB-ICA): an approach for detecting inter-brain networks from fNIRS-hyperscanning data.

Abstract

Functional near-infrared spectroscopy (fNIRS) -based hyperscanning is a popular new technology in the field of social neuroscience research. In recent years, studying human social interaction from the perspective of inter-brain networks has received increasing attention. In the present study, we proposed a new approach named the hyper-brain independent component analysis (HB-ICA) for detecting the inter-brain networks from fNIRS-hyperscanning data. HB-ICA is an ICA-based, data-driven method, and can be used to search the inter-brain networks of social interacting groups containing multiple participants. We validated the method by using both simulated data and in vivo fNIRS-hyperscanning data. The results showed that the HB-ICA had good performance in detecting the inter-brain networks in both simulation and in-vivo experiments. Our approach provided a promising tool for studying the neural mechanism of human social interactions.