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Journal Article

DEEP: A dual EEG pipeline for developmental hyperscanning studies

MPS-Authors

Kayhan,  Ezgi
Department of Developmental Psychology, University of Potsdam, Germany;
Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Matthes,  Daniel
Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Laboratory for Biosignal Processing (LaBP), University of Applied Sciences, Leipzig, Germany;

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Michel,  Christine
Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Faculty of Education, University of Leipzig, Germany;

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Langeloh,  Miriam
Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Psychology, University of Heidelberg, Germany;

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Kayhan_2022.pdf
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Citation

Kayhan, E., Matthes, D., Marriott Haresign, I., Bánki, A., Michel, C., Langeloh, M., et al. (2022). DEEP: A dual EEG pipeline for developmental hyperscanning studies. Developmental Cognitive Neuroscience, 54: 101104. doi:10.1016/j.dcn.2022.101104.


Cite as: http://hdl.handle.net/21.11116/0000-000A-3D7E-6
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.