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

Abstract

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.

Applying functional near-infrared spectroscopy and eye-tracking in a naturalistic educational environment to investigate physiological aspects that underlie the cognitive effort of children during mental rotation tests

“…There seems to be a lack of evidence for neural correlates of children’s cognitive effort and MRT, maybe for neuroimaging technique limitations on movement artifacts. However, the last few years have shown an increasing technology development that allows studies in schools with children ( Soltanlou et al, 2018a ), which includes naturalistic experimental settings with fNIRS ( Ferrari and Quaresima, 2012 ; Balardin et al, 2017 ; Barreto et al, 2021 ) and Eye-Tracking ( Epelboim and Suppes, 2001 ; Bolden et al, 2015 ) outside the laboratory environment. Our study is in line with the idea that fNIRS and eye-tracking are suitable for measuring the cognitive processes of schoolchildren in naturalistic settings ( Mücke et al, 2018 ), and eye-tracking is a valuable device to use to measure physiological activations during spatial cognition stimuli in a familiar environment.…”

Optical imaging and spectroscopy for the study of the human brain: status report

“…The main progress in fNIRS applications in education is the exploration of interpersonal interactions during learning using hyperscanning. 322 , 350 These situations connect with pedagogical constructs such as Vygotsky’s proximal development zone. Moreover, combining fNIRS signals and machine learning for mental states decoding made it possible to monitor and quantify attention, engagement, and learning.…”

“…The ultra-portable wearable and wireless NIRS devices already mentioned overcome limitations and constraints imposed by traditional neuroimaging tools on experimental protocols, data collection settings, and task conditions at the expense of ecological validity. 16 , 317 – 319 Recent fNIRS studies demonstrate examples of experimental scenarios that were not feasible until recently, such as speaker-listener coupling, 320 – 322 mental workload decoding in real-time during a flight over the clouds, 323 or while walking outdoors. 324 , 325 In addition, fNIRS can provide continuous and repeated measurement for longitudinal evaluation, and this can be used not only to assess but also to enhance the training/skill acquisition for complex tasks 326 or to provide online real-time feedback as clinical intervention.…”

Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms

“…Five pairs of teacher-child which reported no cognitive disabilities participated in the experiment, as described in Barreto et al ( 2021 ): Five children aged between 3 and 5 (four boys) and four adults aged between 21 and 28 (two males). Children were recruited by advertisements in a public school and teachers were tutors from a Science Museum at the University of São Paulo, Brazil.…”