Dyadic Sex Composition and Task Classification Using fNIRS Hyperscanning Data. (arXiv:2112.03911v1 [eess.IV])

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.

Source: https://arxiv.org/abs/2112.03911


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