Lore, S.; Julihn, C.; Telfer, P.; Scheibye-Knudsen, M.; Verdin, E.

Biological brain aging is a major determinant of cognitive decline and neurodegenerative disease, yet scalable and intervention-ready brain aging biomarkers remain limited. Here, we develop an electroencephalography (EEG)-based brain age clock using machine learning trained on high-dimensional neural features across the adult lifespan. Using 643 features captured from a Sens.ai headset and controller device, the model predicts chronological age with high accuracy (Pearson r = 0.92; MAE = 4.43 years) and yields an interpretable set of age-informative neural features capturing functional signatures of brain aging. Unlike MRI-based approaches, this EEG-based clock is non-invasive, transportable, cost- effective and suitable for repeated at-home longitudinal measurement. Furthermore, in a longitudinal neuromodulation program, BrainYears-predicted brain age decreased by a mean of -5.18 years in the intervention group whereas a minimal-exposure comparison group showed no change on average (+0.07 years). Together, this work introduces a functional brain aging biomarker and an intervention-ready platform for quantifying brain age modulation.