Frontiers in Aging · Nov 2, 2026
Estimating Brain Ageing in Healthy Populations: The Role of EEG-Based Approaches
By Anel N Tormanova, Aigul Nurmaganbetova, Altyngul Kamzanova et al.
This structured narrative review synthesizes evidence on electroencephalography-based approaches to the assessment of brain ageing in healthy populations. It examines normative age-related electroencephalography (EEG) changes, prediction of chronological age from EEG-derived features, model validation, and the potential clinical relevance of brain-age estimates. Resting-state EEG findings most consistently indicate a decline in peak alpha frequency with advancing age, together with changes in alpha power and reactivity. Evidence concerning delta and theta activity, functional connectivity, aperiodic parameters, entropy, complexity, and microstate dynamics is more heterogeneous and depends on sample characteristics, recording conditions, preprocessing, feature definitions, and analytical methods. Event-related potentials provide complementary information on age-related changes in attention, novelty processing, semantic integration, and executive control, although they have rarely been incorporated into existing brain-age models. Current approaches include models based on engineered EEG features and end-to-end deep-learning methods applied to raw or minimally processed signals. Selected studies report mean absolute errors of approximately 5–7 years, but cross-study comparisons are limited by differences in cohorts, protocols, validation strategies, and age-bias correction. Overall, EEG is a feasible and scalable modality for characterizing neurophysiological ageing, but it is not yet a validated biomarker for individual clinical classification. Further progress requires external and subject-independent validation, correction of regression-to-the-mean bias, diverse normative cohorts, longitudinal designs, and integration with cognitive, clinical, imaging, and genetic data.