Dyadic Sex Composition and Task Classification Using fNIRS Hyperscanning Data

TL;DR

This paper introduces a deep learning method using CNNs to classify dyadic sex composition and task type from fNIRS hyperscanning data, achieving over 80% accuracy and advancing social neuroscience analysis.

Contribution

It is the first to apply deep learning to classify sex and task differences in fNIRS hyperscanning data, utilizing inter-brain signal similarity measures.

Findings

Achieved over 80% classification accuracy.

Demonstrated the effectiveness of CNNs on hyperscanning data.

Provided new insights into neural signatures of social interactions.

Abstract

Hyperscanning with functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging application that measures the nuanced neural signatures underlying social interactions. Researchers have assessed the effect of sex and task type (e.g., cooperation versus competition) on inter-brain coherence during human-to-human interactions. However, no work has yet used deep learning-based approaches to extract insights into sex and task-based differences in an fNIRS hyperscanning context. This work proposes a convolutional neural network-based approach to dyadic sex composition and task classification for an extensive hyperscanning dataset with $N = 222$ participants. Inter-brain signal similarity computed using dynamic time warping is used as the input data. The proposed approach achieves a maximum classification accuracy of greater than $80$ percent, thereby providing a new avenue for exploring and understanding complex brain behavior.