Research progress in electroencephalogram-based brain age prediction.
10.7507/1001-5515.202503043
- Author:
Hongyue ZU
1
;
Ping ZHAN
1
;
Hui YU
1
;
Weidong WANG
1
;
Hongyun LIU
1
Author Information
1. Medical Innovation & Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. China.
- Publication Type:English Abstract
- Keywords:
Brain age prediction;
Clinical application;
Deep learning;
Electroencephalogram;
Machine learning
- MeSH:
Humans;
Electroencephalography/methods*;
Brain/physiology*;
Machine Learning;
Aging/physiology*;
Deep Learning;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2025;42(4):832-840
- CountryChina
- Language:Chinese
-
Abstract:
Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability. This review comprehensively examined the advancements in EEG-based brain age prediction, detailing key aspects of data preprocessing, feature extraction, model construction, and result evaluation. It also summarized the current applications of machine learning and deep learning methods in this field, analyzed existing issues, and explored future directions to promote the widespread application of EEG-based brain age prediction in both clinical and research settings.
