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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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Developmental Cognitive Neuroscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929324001191 |
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| author | Wenyi Xu Alexa D. Monachino Sarah A. McCormick Emma T. Margolis Ana Sobrino Cara Bosco Cassandra J. Franke Lauren Davel Michal R. Zieff Kirsten A. Donald Laurel J. Gabard-Durnam Santiago Morales |
| author_facet | Wenyi Xu Alexa D. Monachino Sarah A. McCormick Emma T. Margolis Ana Sobrino Cara Bosco Cassandra J. Franke Lauren Davel Michal R. Zieff Kirsten A. Donald Laurel J. Gabard-Durnam Santiago Morales |
| author_sort | Wenyi Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c95a96aff5ea46dc91686b0f23bdd6d4 |
| institution | OA Journals |
| issn | 1878-9293 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj-art-c95a96aff5ea46dc91686b0f23bdd6d42025-08-20T02:07:34ZengElsevierDevelopmental Cognitive Neuroscience1878-92932024-12-017010145810.1016/j.dcn.2024.101458Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metricsWenyi Xu0Alexa D. Monachino1Sarah A. McCormick2Emma T. Margolis3Ana Sobrino4Cara Bosco5Cassandra J. Franke6Lauren Davel7Michal R. Zieff8Kirsten A. Donald9Laurel J. Gabard-Durnam10Santiago Morales11Department of Psychology, University of Southern California, USA; Correspondence to: University of Southern California, 501 Seeley G. Mudd Building, Los Angeles 90089, USA.Department of Psychology, University of Southern California, USADepartment of Psychology, Northeastern University, USADepartment of Psychology, Northeastern University, USADepartment of Psychology, Northeastern University, USADepartment of Psychology, Northeastern University, USADepartment of Psychology, Northeastern University, USADepartment of Pediatrics and Child Health, University of Cape Town, USADepartment of Pediatrics and Child Health, University of Cape Town, USADepartment of Pediatrics and Child Health, University of Cape Town, USADepartment of Psychology, Northeastern University, USADepartment of Psychology, University of Southern California, USA; Correspondence to: University of Southern California, 501 Seeley G. Mudd Building, Los Angeles 90089, USA.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.http://www.sciencedirect.com/science/article/pii/S1878929324001191Electroencephalogram (EEG)Data quality metricsEvent-related potentials (ERPs)ReliabilityStandard Measurement Error (SME)Effect sizes |
| spellingShingle | Wenyi Xu Alexa D. Monachino Sarah A. McCormick Emma T. Margolis Ana Sobrino Cara Bosco Cassandra J. Franke Lauren Davel Michal R. Zieff Kirsten A. Donald Laurel J. Gabard-Durnam Santiago Morales Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics Developmental Cognitive Neuroscience Electroencephalogram (EEG) Data quality metrics Event-related potentials (ERPs) Reliability Standard Measurement Error (SME) Effect sizes |
| title | Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics |
| title_full | Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics |
| title_fullStr | Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics |
| title_full_unstemmed | Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics |
| title_short | Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics |
| title_sort | advancing the reporting of pediatric eeg data tools for estimating reliability effect size and data quality metrics |
| topic | Electroencephalogram (EEG) Data quality metrics Event-related potentials (ERPs) Reliability Standard Measurement Error (SME) Effect sizes |
| url | http://www.sciencedirect.com/science/article/pii/S1878929324001191 |
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