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.
With the increasing aging worldwide, the age-related neurodegenerative diseases are becoming more and more prevalent. Brain age, as a critical biological marker for assessing normal brain aging and indicating disease progression, has been widely applied in the early diagnosis and evaluation of neurodegenerative diseases such as Parkinson’s disease (PD). This paper systematically elaborates on three types of methods for PD brain age prediction: statistical methods, traditional Machine learning (ML), and Deep learning (DL), from the perspectives of methodological overview and clinical application of PD brain age predication. For the first aspect, the PD brain age prediction workflow, statistical methods, ML methods, and DL methods are sequentially outlined; in the second aspect, the current clinical application status of the three types of PD brain age prediction methods is introduced. Finally, a summary and outlook are provided. This review not only provides important references for research on PD brain age prediction, but also offers novel approaches for evaluating human brain health, thus holding significant scientific and clinical value.