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DEEP: A dual EEG pipeline for developmental hyperscanning studies

Authors :
Kayhan, Ezgi
Matthes, Daniel
Marriott Haresign, Ira
Bánki, Anna
Michel, Christine
Langeloh, Miriam
Wass, Sam
Hoehl, Stefanie
Morales, Santiago
Buzzell, George (PhD)
Valadez, Emilio (PhD)
Fox, Nathan
Hunnius, Sabine (M.Sc.)
Publication Year :
2022
Publisher :
Universitätsverlag Potsdam, 2022.

Abstract

Cutting-edge hyperscanning methods led to a paradigm shift in social neuroscience. It allowed researchers to measure dynamic mutual alignment of neural processes between two or more individuals in naturalistic contexts. The ever-growing interest in hyperscanning research calls for the development of transparent and validated data analysis methods to further advance the field. We have developed and tested a dual electroencephalography (EEG) analysis pipeline, namely DEEP. Following the preprocessing of the data, DEEP allows users to calculate Phase Locking Values (PLVs) and cross-frequency PLVs as indices of inter-brain phase alignment of dyads as well as time-frequency responses and EEG power for each participant. The pipeline also includes scripts to control for spurious correlations. Our goal is to contribute to open and reproducible science practices by making DEEP publicly available together with an example mother-infant EEG hyperscanning dataset.<br />Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe; 799

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....8badb85e8d3c929201b85b5fc57c8424
Full Text :
https://doi.org/10.25932/publishup-56689