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A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s
Journal article   Open access   Peer reviewed

A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s

Candida Barreto, Guilherme de Albuquerque Bruneri, Guilherme Brockington, Hasan Ayaz and Joao Ricardo Sato
Frontiers in human neuroscience, v 15, 622146
07 May 2021
DOI: https://doi.org/10.3389/fnhum.2021.622146
PMID: 34025373
url
https://doi.org/10.3389/fnhum.2021.622146View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

fNIRS Human Neuroscience hyperscanning support vector regression teacher-student interaction Machine Learning
Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression (SVR) to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the teacher and preschoolers’ dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student’s signal as the response variable and the teacher’s ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square (OLS) predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads (18 channels 5 dyads = 90 channel-pairs) presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers’ cortical activity can be used to predict the student brain hemodynamic response.

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22 citations in Web of Science
23 citations in Scopus

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Domestic collaboration
International collaboration
Web of Science research areas
Neurosciences
Psychology
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