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      A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis

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          Abstract

          The use of functional near-infrared spectroscopy (fNIRS) hyperscanning during naturalistic interactions in parent–child dyads has substantially advanced our understanding of the neurobiological underpinnings of human social interaction. However, despite the rise of developmental hyperscanning studies over the last years, analysis procedures have not yet been standardized and are often individually developed by each research team. This article offers a guide on parent–child fNIRS hyperscanning data analysis in MATLAB and R. We provide an example dataset of 20 dyads assessed during a cooperative versus individual problem-solving task, with brain signal acquired using 16 channels located over bilateral frontal and temporo-parietal areas. We use MATLAB toolboxes Homer2 and SPM for fNIRS to preprocess the acquired brain signal data and suggest a standardized procedure. Next, we calculate interpersonal neural synchrony between dyads using Wavelet Transform Coherence (WTC) and illustrate how to run a random pair analysis to control for spurious correlations in the signal. We then use RStudio to estimate Generalized Linear Mixed Models (GLMM) to account for the bounded distribution of coherence values for interpersonal neural synchrony analyses. With this guide, we hope to offer advice for future parent–child fNIRS hyperscanning investigations and to enhance replicability within the field.

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          Application of the cross wavelet transform and wavelet coherence to geophysical time series

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            HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.

            Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.
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              OpenSesame: An open-source, graphical experiment builder for the social sciences

              In the present article, we introduce OpenSesame, a graphical experiment builder for the social sciences. OpenSesame is free, open-source, and cross-platform. It features a comprehensive and intuitive graphical user interface and supports Python scripting for complex tasks. Additional functionality, such as support for eyetrackers, input devices, and video playback, is available through plug-ins. OpenSesame can be used in combination with existing software for creating experiments.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                13 June 2021
                June 2021
                : 21
                : 12
                : 4075
                Affiliations
                [1 ]Department of Developmental and Educational Psychology, University of Vienna, 1010 Vienna, Austria; stefanie.hoehl@ 123456univie.ac.at
                [2 ]Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
                [3 ]Department of Psychology, University of Essex, Colchester CO4 3SQ, UK; p.vrticka@ 123456essex.ac.uk
                Author notes
                Author information
                https://orcid.org/0000-0003-0420-2147
                https://orcid.org/0000-0003-0472-0374
                https://orcid.org/0000-0002-8920-5509
                Article
                sensors-21-04075
                10.3390/s21124075
                8231828
                34199222
                373c5365-6bd9-48f8-b369-e3fb399f814d
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 13 April 2021
                : 11 June 2021
                Categories
                Article

                Biomedical engineering
                fnirs,hyperscanning,synchrony
                Biomedical engineering
                fnirs, hyperscanning, synchrony

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