Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

Abstract

With an aging global population, the number of older adults with age‐related changes in the brain, including dementia, will continue to increase unless we can make progress in the early detection and treatment of such conditions. There is extensive literature on the effects of aging on the EEG, particularly a decline in the Peak Alpha Frequency (PAF), but here, in a reversal of convention, we used the EEG power‐frequency spectrum to estimate chronological age. The motivation for this approach was that an individual's brain age might act as a proxy for their general brain functioning, whereby a discrepancy between chronological age and EEG age could prove clinically informative by implicating deleterious conditions. With a sample of sixty healthy adults, whose ages ranged from 20 to 78 years, and using multivariate methods to analyze the broad EEG spectrum (0.1–45 Hz), strong positive correlations between chronological age and EEG age emerged. Furthermore, EEG age was a more accurate estimate and accounted for more variance in chronological age than well‐established PAF‐based estimates of age, indicating that EEG age could be a more comprehensive measure of general brain functioning. We conclude that EEG age could become a biomarker for neural and cognitive integrity.

Publication DOI: https://doi.org/10.1111/psyp.70033
Divisions: College of Health & Life Sciences > School of Psychology
Additional Information: Copyright © 2025 The Author(s). Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: human lifespan,EEG power Spectrum,brain integrity,partial least squares regression,resting state electroencephalography,aging brain,dementia,peak alpha frequency
Publication ISSN: 1469-8986
Data Access Statement: Data are available at https:// osf. io/ 46pzm ; a preprint is available at PsyArXiv (https:// psyar xiv. com/ 4sfdm ).
Last Modified: 02 Apr 2025 07:26
Date Deposited: 18 Mar 2025 08:42
Full Text Link:
Related URLs: https://onlinel ... 1111/psyp.70033 (Publisher URL)
PURE Output Type: Article
Published Date: 2025-03
Published Online Date: 2025-03-16
Accepted Date: 2025-02-20
Authors: James, Thomas M.
Burgess, Adrian P.

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License: Creative Commons Attribution


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