Application of electroencephalogram for depression and anxiety identification
10.3760/cma.j.cn371468-202400603-00256
- VernacularTitle:脑电信号在抑郁和焦虑障碍识别中的应用
- Author:
Shu JING
1
;
Zhenwei DAI
;
Xiaoyou SU
Author Information
1. 中国医学科学院北京协和医学院群医学及公共卫生学院，北京 100010
- Publication Type:Journal Article
- Keywords:
Electroencephalogram;
Depression;
Anxiety;
Machine learning
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2025;34(1):89-94
- CountryChina
- Language:Chinese
-
Abstract:
Depression and anxiety are the two most common mental disorders, which have severe negative impacts on individuals and society. Early and accurate identification of these disorders is crucial for symptom control and positive prognosis. Traditional identification approaches such as scale screening and interviews rely on patients' self-reporting and the comprehensive judgment of psychiatrists. These methods inherently have limitations such as subjectivity and dependence on the availability and convenience of medical resources. Therefore, seeking objective means to assist in the identification of depression and anxiety disorders has become a research focus in recent years. Electroencephalogram (EEG), which describes changes in potential difference on the scalp surface, offers advantages such as objectivity, quantification and high temporal resolution. EEG has become one of the potential objective indicators for identifying depression and anxiety disorders.Machine learning algorithms are key technologies for extracting EEG signal features and improving identification accuracy. The combination of EEG collection and machine learning algorithms is expected to become a new method for identifying depression and anxiety disorders. In addition, portable EEG collection provides the possibility for rapid identification of mental disorders and large-scale screening activities. This article reviews the application of EEG for depression and anxiety disorders identification, providing a reference for scholars in related fields, with the aim of promoting the development of mental disorder identification towards objectivity and visualization in China.
