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

Resting electroencephalography (EEG) has been posited as a biomarker for children's cognitive and social development. However, substantial heterogeneity in current EEG data collection, preprocessing, and analytic approaches lowers interpretability and reproducibility across studies. This systematic review aims to identify existing methodological variability and consensus in the recent developmental EEG literature and explore whether any differences can be explained by the age of the sample, year of publication, or the resting paradigm. Reviewed articles were drawn from six journals notable for publishing pediatric EEG research between 2011 and 2023. We screened over 650 articles, resulting in 56 articles for further data extraction. Extracted information included: sample characteristics (e.g. age), data collection paradigm (e.g., resting paradigm), preprocessing and signal analysis steps (e.g., power spectra transformations, frequency band boundaries), and statistical analysis methods (e.g., multiple comparison corrections). Our results highlight substantial differences in data collection, preprocessing, and analytic steps that are largely not explained by sample age, publication year, or resting paradigm. Collectively, our findings emphasize the need for increased standardization in data collection, preprocessing, and analysis methods to ensure future developmental EEG research is reproducible and reliable. We provide a suggested checklist for researchers detailing what to report when publishing resting EEG research and suggest collaborative efforts to establish consensus.