[Research progress in electroencephalogram-based brain age prediction].

Area of Science:

• Neuroscience
• Medical Imaging
• Artificial Intelligence

Background:

• Brain age prediction is crucial for evaluating brain health and detecting neurodegenerative diseases early.
• Electroencephalogram (EEG) offers a non-invasive, cost-effective method for brain age prediction due to its high temporal resolution and correlation with brain function.

Purpose of the Study:

• To comprehensively review advancements in EEG-based brain age prediction.
• To detail data preprocessing, feature extraction, model construction, and evaluation methods.
• To summarize machine learning and deep learning applications, identify challenges, and suggest future research directions.

Main Methods:

• Review of existing literature on EEG-based brain age prediction.
• Analysis of data preprocessing techniques for EEG signals.
• Examination of various machine learning and deep learning models used for prediction.
• Evaluation of common metrics for assessing prediction performance.

Main Results:

• Significant progress has been made in enhancing the accuracy and generalizability of EEG-based brain age prediction models.
• Challenges persist regarding EEG data quality and the interpretability of prediction models.
• Machine learning and deep learning approaches have shown considerable success in this domain.

Conclusions:

• EEG-based brain age prediction is a rapidly advancing field with high potential for clinical and research applications.
• Addressing data quality and model interpretability are key for future development.
• Further research is needed to optimize models and facilitate widespread adoption.