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  Enhancing reproducibility in developmental EEG research: BIDS, cluster-based permutation tests, and effect sizes

Meyer, M., Lamers, D., Kayhan, E., Hunnius, S., & Oostenveld, R. (2021). Enhancing reproducibility in developmental EEG research: BIDS, cluster-based permutation tests, and effect sizes. Developmental Cognitive Neuroscience, 52: 101036. doi:10.1016/j.dcn.2021.101036.

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 Creators:
Meyer, Marlene1, 2, Author
Lamers, Didi3, Author
Kayhan, Ezgi4, 5, Author              
Hunnius, Sabine1, Author
Oostenveld, Robert1, 6, Author
Affiliations:
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands, ou_persistent22              
2Department of Psychology, University of Chicago, IL, USA, ou_persistent22              
3Radboud University, Nijmegen, the Netherlands, ou_persistent22              
4Department of Developmental Psychology, University of Potsdam, Germany, ou_persistent22              
5Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2355694              
6NatMEG, Karolinska Institute, Stockholm, Sweden, ou_persistent22              

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Free keywords: EEG; Reproducibility; Cluster-based permutation test; Effect size; BIDS
 Abstract: Developmental research using electroencephalography (EEG) offers valuable insights in brain processes early in life, but at the same time, applying this sensitive technique to young children who are often non-compliant and have short attention spans comes with practical limitations. It is thus of particular importance to optimally use the limited resources to advance our understanding of development through reproducible and replicable research practices. Here, we describe methodological approaches that help maximize the reproducibility of developmental EEG research. We discuss how to transform EEG data into the standardized Brain Imaging Data Structure (BIDS) which organizes data according to the FAIR data sharing principles. We provide a tutorial on how to use cluster-based permutation testing to analyze developmental EEG data. This versatile test statistic solves the multiple comparison problem omnipresent in EEG analysis and thereby substantially decreases the risk of reporting false discoveries. Finally, we describe how to quantify effect sizes, in particular of cluster-based permutation results. Reporting effect sizes conveys a finding’s impact and robustness which in turn informs future research. To demonstrate these methodological approaches to data organization, analysis and report, we use a publicly accessible infant EEG dataset and provide a complete copy of the analysis code.

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Language(s): eng - English
 Dates: 2021-10-292021-05-302021-11-112021-11-122021-12
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.dcn.2021.101036
Other: epub 2021
PMID: 34801856
PMC: PMC8607163
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Title: Developmental Cognitive Neuroscience
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 52 Sequence Number: 101036 Start / End Page: - Identifier: ISSN: 1878-9293
CoNE: https://pure.mpg.de/cone/journals/resource/1878-9293