Back to the Future of qEEG: Lifespan Normative Modeling of Spectral Ratios and Functional Indices with Potential Applications to Therapeutic Monitoring.

Quantitative EEG (qEEG) provides objective, millisecond-resolution measures of brain dynamics. Despite decades of methodological advances, clinically relevant derived indices-spectral power ratios, cognitive-emotional state markers, and physiological parameters-are typically reported as raw values without the normative context required for individualized clinical inference. To develop the first systematic age-dependent normative models for this family of derived qEEG indices using a multinational database, enabling probabilistic Z-score interpretation at the individual level with potential applications in objective therapeutic monitoring. Normative modeling was applied to the HarMNqEEG database (n = 1,564 neurologically healthy participants, ages 5-97, 9 countries, eyes-closed resting state). Electrode-level Spectral Normalization (ESN) removed inter-individual and inter-device amplitude variability while preserving the neurophysiological interpretability of each index. Age-dependent normative trajectories were estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS) with P-splines on log(age), allowing conditional mean and variance to vary non-linearly across the lifespan. GAMLSS modeling revealed significant non-linear age-dependent trajectories for all indices. Slow-wave-dominated ratios showed steep decreases from childhood to early adulthood, consistent with cortical maturation; alpha-dominated indices increased during adolescence before stabilizing. ESN normalization yielded well-calibrated normative residuals across the full age range for all indices, except Valence. For the Valence index, a quantile regression model is provided as the recommended normative reference due to its spike-and-slab marginal distribution. These normative models provide a principled, age-adjusted probabilistic framework for individual-level qEEG interpretation, based on eyes closed resting state recordings, laying the methodological groundwork for future clinical validation in diagnostic and therapeutic monitoring applications. The ESN strategy requires no knowledge of recording equipment, ensuring broad applicability across clinical and research settings.