Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics

EEG studies play a crucial role in enhancing our understanding of brain development across the lifespan. The increasing clinical and policy implications of EEG research underscore the importance of utilizing reliable EEG measures and increasing the reproducibility of EEG studies. However, important...

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Bibliographic Details
Published in:Developmental cognitive neuroscience 2024-12, Vol.70, p.101458, Article 101458
Main Authors: Xu, Wenyi, Monachino, Alexa D., McCormick, Sarah A., Margolis, Emma T., Sobrino, Ana, Bosco, Cara, Franke, Cassandra J., Davel, Lauren, Zieff, Michal R., Donald, Kirsten A., Gabard-Durnam, Laurel J., Morales, Santiago
Format: Article
Language:English
Subjects:
Brain - physiology
Child
Data Accuracy
Data quality metrics
Effect sizes
Electroencephalogram (EEG)
Electroencephalography - methods
Event-related potentials (ERPs)
Humans
Reliability
Reproducibility of Results
Software
Standard Measurement Error (SME)
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Summary:EEG studies play a crucial role in enhancing our understanding of brain development across the lifespan. The increasing clinical and policy implications of EEG research underscore the importance of utilizing reliable EEG measures and increasing the reproducibility of EEG studies. However, important data characteristics like reliability, effect sizes, and data quality metrics are often underreported in pediatric EEG studies. This gap in reporting could stem from the lack of accessible computational tools for quantifying these metrics for EEG data. To help address the lack of reporting, we developed a toolbox that facilitates the estimation of internal consistency reliability, effect size, and standardized measurement error with user-friendly software that facilitates both computing and interpreting these measures. In addition, our tool provides subsampled reliability and effect size in increasing numbers of trials. These estimates offer insights into the number of trials needed for detecting significant effects and reliable measures, informing the minimum number of trial thresholds for the inclusion of participants in individual difference analyses and the optimal trial number for future study designs. Importantly, our toolbox is integrated into commonly used preprocessing pipelines to increase the estimation and reporting of data quality metrics in developmental neuroscience.
ISSN:1878-9293
1878-9307
1878-9307
DOI:10.1016/j.dcn.2024.101458