Related Experiment Video
Updated: Mar 10, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
Published on: June 30, 2014
Power struggles: Absolute vs. relative EEG power in developmental neuroscience
Aislinn Sandre1, Sonya V Troller-Renfree2
1Department of Psychology, University of Western Ontario, London, ON N6G 2V4, Canada.
None:
Resting electroencephalography (EEG) is a central tool for studying early brain function and development. Yet, a key methodological decision-whether to quantify spectral activity using absolute or relative power-remains inconsistently applied and theoretically underdeveloped. Absolute power indexes the raw amplitude of oscillatory activity, whereas relative power expresses each frequency band as a proportion of the total signal. Relative power is often assumed to control for non-neural variability (e.g., hair texture), but this assumption has rarely been evaluated, particularly during periods of rapid developmental change. This commentary integrates conceptual analysis with empirical examples from multiple pediatric samples to evaluate whether relative power is indeed less biased by common sources of non-neural variability. Across frequency bands, absolute and relative measures showed both convergence and divergence: higher-frequency activity (e.g., gamma) aligned across indices, whereas lower-frequency activity (e.g., theta) did not, suggesting distinct neurophysiological and developmental properties. Relative power dampened some amplitude-related effects (e.g., fatigue), but remained influenced by hair texture, affect, and time of day. Together, these findings indicate that relative power does not universally correct for non-neural or state-related variability but instead provides a complementary representation of spectral composition. We recommend that developmental EEG studies report and interpret both absolute and relative power, justify analytic choices, and account for biological and contextual covariates. Greater clarity and consistency in how these metrics are used will improve the interpretability, reproducibility, and developmental relevance of EEG findings.

