Dyadic Sex Composition and Task Classification Using fNIRS Hyperscanning Data

by   Liam A. Kruse, et al.

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.


page 3

page 4

page 6


Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Handwriting-based gender classification is a well-researched problem tha...

Region extraction based approach for cigarette usage classification using deep learning

This paper has proposed a novel approach to classify the subjects' smoki...

Transformer-Based Hierarchical Clustering for Brain Network Analysis

Brain networks, graphical models such as those constructed from MRI, hav...

Using Physiological Information to Classify Task Difficulty in Human-Swarm Interaction

Human-swarm interaction has recently gained attention due to its plethor...

Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Midline shift (MLS) is a well-established factor used for outcome predic...

Please sign up or login with your details

Forgot password? Click here to reset