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

Thomas M. James*, Adrian P. Burgess*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article numbere70033
Number of pages21
JournalPsychophysiology
Volume62
Issue number3
Early online date16 Mar 2025
DOIs
Publication statusPublished - Mar 2025

Bibliographical note

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.

Data Access Statement

Data are available at https:// osf. io/ 46pzm ; a preprint is available at PsyArXiv (https:// psyar xiv. com/ 4sfdm ).

Keywords

  • human lifespan
  • EEG power Spectrum
  • brain integrity
  • partial least squares regression
  • resting state electroencephalography
  • aging brain
  • dementia
  • peak alpha frequency

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