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Hyperscanning with functional near-infrared spectroscopy (fNIRS) is anemerging neuroimaging application that measures the nuanced neural signaturesunderlying social interactions. Researchers have assessed the effect of sex andtask type (e.g., cooperation versus competition) on inter-brain coherenceduring human-to-human interactions. However, no work has yet used deeplearning-based approaches to extract insights into sex and task-baseddifferences in an fNIRS hyperscanning context. This work proposes aconvolutional neural network-based approach to dyadic sex composition and taskclassification for an extensive hyperscanning dataset with $N = 222$participants. Inter-brain signal similarity computed using dynamic time warpingis used as the input data. The proposed approach achieves a maximumclassification accuracy of greater than $80$ percent, thereby providing a newavenue for exploring and understanding complex brain behavior.

Dyadic Sex Composition and Task Classification Using fNIRS Hyperscanning Data