Wavelet Transform Coherence: An Innovative Method to Investigate Social Interaction in NeuroIS

Paul Léné (), Alexander J. Karran (), Elise Labonté-Lemoyne (), Sylvain Sénécal (), Marc Fredette (), Kevin J. Johnson () and Pierre-Majorique Léger ()
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Paul Léné: HEC Montréal
Alexander J. Karran: HEC Montréal
Elise Labonté-Lemoyne: HEC Montréal
Sylvain Sénécal: HEC Montréal
Marc Fredette: HEC Montréal
Kevin J. Johnson: HEC Montréal
Pierre-Majorique Léger: HEC Montréal

A chapter in Information Systems and Neuroscience, 2020, pp 147-154 from Springer

Abstract: Abstract Neuro-information system researchers called for the development of tools and methods of analysis that allows for the assessment of social interactions that considers the full range of dynamics and complexities. Recent work and progress in hyperscanning—the simultaneous recording of multiple subjects—offers the possibility to develop such methods and new research paradigms to respond to that need. Among these methods, Wavelet Transform Coherence (WTC) analysis is gaining in popularity and accessibility. However, hyperscanning methods—including WTC—remain a challenging experimental paradigm and analysis method, requiring careful preparation of data and consideration of the constraints of each neuroimaging technique. This manuscript aims to introduce the Wavelet Transform Coherence method of analysis as an innovative approach to Neuro-Information Systems research. We present practical examples and results in order to highlight the potential and complexity of this approach.

Keywords: EEG; Neuro-information system; Social neuroscience; Hyperscanning; Wavelet transform coherence; Wavelet analysis (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-28144-1_16

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DOI: 10.1007/978-3-030-28144-1_16

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