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Related Experiment Video

Updated: May 25, 2026

Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia
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Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia

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Pediatric resting EEG collection, preprocessing, and analysis: A systematic review.

Kathryn N Gray1, Haleigh M Brown1, Agostina Mannara1

  • 1Department of Human Development, Teachers College, Columbia University, New York, NY, USA.

Developmental Cognitive Neuroscience
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Resting electroencephalography (EEG) shows promise for tracking child development, but inconsistent methods hinder research. Standardization is crucial for reproducible results in developmental EEG studies.

Keywords:
AlphaBaselineBetaEEGGammaRestingResting StateTheta

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Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Biomarkers

Background:

  • Resting electroencephalography (EEG) is a potential biomarker for assessing cognitive and social development in children.
  • Significant variability in data collection, preprocessing, and analysis methods across studies compromises the interpretability and reproducibility of developmental EEG research.

Purpose of the Study:

  • To systematically review recent developmental EEG literature (2011-2023) to identify methodological variability and consensus.
  • To explore if variations in methods are explained by sample age, publication year, or resting paradigm used.

Main Methods:

  • Systematic review of 56 articles from six prominent pediatric EEG journals.
  • Data extraction focused on sample characteristics, data collection paradigms, preprocessing steps, signal analysis, and statistical methods.
  • Analysis explored methodological differences in relation to sample age, publication year, and resting paradigm.

Main Results:

  • Substantial heterogeneity was found in data collection, preprocessing, and analysis techniques within developmental EEG studies.
  • These methodological differences were largely not explained by sample age, publication year, or the specific resting paradigm employed.
  • The review identified a lack of consensus in key methodological areas crucial for EEG research.

Conclusions:

  • There is a critical need for standardization in data collection, preprocessing, and analysis to enhance the reproducibility and reliability of developmental EEG research.
  • The study provides a checklist for researchers to improve reporting standards in resting EEG publications.
  • Collaborative efforts are recommended to establish consensus on best practices for developmental EEG research.