Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso.

ABSTRACT:

Introduction

The neural dynamics underlying cognition and behavior change greatly during the lifespan of brain development and aging. EEG is a promising modality due to its high temporal resolution in capturing neural oscillations. Precise prediction of brain age (BA) based on EEG is crucial to screening high-risk individuals from large cohorts. However, the lifespan representation of the EEG oscillatory features (OSFs) is largely unclear, limiting practical BA applications in clinical scenarios. This study aims to build an interpretable BA prediction model through prior knowledge and sparse group lasso.

Methods

Based on the multinational cross-spectral (MNCS) dataset that covers 5-97 years, (1) we extracted four groups of OSFs, such as aperiodic parameters, periodic paramet

SUBMITTER: Hu S 

PROVIDER: S-EPMC12321796 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature