Test-Retest Reliability Analysis of Resting-state EEG Measures and Their Association with Long-Term Memory in Children and Adults

Abstract

EEG resting-state measures, such as spectral power and microstates, have been associated with human long-term memory (LTM) performance. However, findings across studies are inconsistent and sometimes contradictory, likely due to a low reliability of the measures employed. These inconsistencies limit the interpretability and generalizability of results, emphasizing the need for a systematic evaluation of measure reliability. In this study, we addressed this gap by identifying the most reliable EEG resting-state measures and evaluating their predictive value for LTM performance in a second-language (L2) vocabulary learning paradigm. A group of children (N = 36) and adults (N = 90) participated in 2 studies on app-assisted learning of second language vocabulary. Participants completed a test on L2 vocabulary and a resting-state EEG recording (180 s eyes open) before and after learning a second language using a smartphone app. We used Intraclass Correlation Coefficients (ICC) to identify resting state EEG measures with satisfying test-retest reliability (ICC >= 0.75) and then assessed how these reliable measures are associated with L2 vocabulary learning representing LTM performance. Highest ICC values were found for oscillatory power in the alpha range and in the frequency of occurrences, duration and coverages of microstates. Calculations yielded ICC values of 0.84/0.86 (children/adults) for alpha power and 0.88/0.80 for microstate measures. Of these measures, only alpha power showed a positive correlation with LTM performance, but only in the adult population (r = 0.38, p 0.05). Alpha power could thus serve as a stable and reliable marker of the neural mechanisms accounting for high LTM performance in the fully developed adult brain.