Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data
Current approaches to analysing EEG hyperscanning data in the developmental literature typically consider interpersonal entrainment between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how entrainment between interacting systems is...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-04-01
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| Series: | Developmental Cognitive Neuroscience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929322000378 |
| _version_ | 1828405773213892608 |
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| author | I. Marriott Haresign E.A.M. Phillips M. Whitehorn L. Goupil V. Noreika V. Leong S.V. Wass |
| author_facet | I. Marriott Haresign E.A.M. Phillips M. Whitehorn L. Goupil V. Noreika V. Leong S.V. Wass |
| author_sort | I. Marriott Haresign |
| collection | DOAJ |
| description | Current approaches to analysing EEG hyperscanning data in the developmental literature typically consider interpersonal entrainment between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how entrainment between interacting systems is established and maintained over time. Here, we describe methods, and present computational algorithms, that will allow researchers to address this gap in the literature. We focus on how two different approaches to measuring entrainment, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality) measures, can be applied to three aspects of the brain signal: amplitude, power, and phase. We guide the reader through worked examples using simulated data on how to leverage these methods to measure changes in interbrain entrainment. For each, we aim to provide a detailed explanation of the interpretation and application of these analyses when studying neural entrainment during early social interactions. |
| first_indexed | 2024-12-10T10:59:21Z |
| format | Article |
| id | doaj.art-0979039cfab94dd9ba38f0bb81697d16 |
| institution | Directory Open Access Journal |
| issn | 1878-9293 |
| language | English |
| last_indexed | 2024-12-10T10:59:21Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj.art-0979039cfab94dd9ba38f0bb81697d162022-12-22T01:51:44ZengElsevierDevelopmental Cognitive Neuroscience1878-92932022-04-0154101093Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning dataI. Marriott Haresign0E.A.M. Phillips1M. Whitehorn2L. Goupil3V. Noreika4V. Leong5S.V. Wass6University of East London, London, UK; Correspondence to: University of East London, London E15 4LZ, UK.University of East London, London, UKUniversity of East London, London, UKUniversity of East London, London, UKDepartment of Biological and Experimental Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UKDepartment of Experimental Psychology, University of Cambridge, Cambridge, UK; School of Social Sciences, Nanyang Technological University, SingaporeUniversity of East London, London, UKCurrent approaches to analysing EEG hyperscanning data in the developmental literature typically consider interpersonal entrainment between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how entrainment between interacting systems is established and maintained over time. Here, we describe methods, and present computational algorithms, that will allow researchers to address this gap in the literature. We focus on how two different approaches to measuring entrainment, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality) measures, can be applied to three aspects of the brain signal: amplitude, power, and phase. We guide the reader through worked examples using simulated data on how to leverage these methods to measure changes in interbrain entrainment. For each, we aim to provide a detailed explanation of the interpretation and application of these analyses when studying neural entrainment during early social interactions.http://www.sciencedirect.com/science/article/pii/S1878929322000378EntrainmentHyperscanningEEGSocial interactionGranger causalityPhase Locking Value |
| spellingShingle | I. Marriott Haresign E.A.M. Phillips M. Whitehorn L. Goupil V. Noreika V. Leong S.V. Wass Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data Developmental Cognitive Neuroscience Entrainment Hyperscanning EEG Social interaction Granger causality Phase Locking Value |
| title | Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data |
| title_full | Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data |
| title_fullStr | Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data |
| title_full_unstemmed | Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data |
| title_short | Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data |
| title_sort | measuring the temporal dynamics of inter personal neural entrainment in continuous child adult eeg hyperscanning data |
| topic | Entrainment Hyperscanning EEG Social interaction Granger causality Phase Locking Value |
| url | http://www.sciencedirect.com/science/article/pii/S1878929322000378 |
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