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The application of electroencephalogram in depression research: bibliometric and technological application analysis
Yican Hao1,2, Yanli Han2, Jian Huang2
1Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
Background:
Depression is a common mental disorder, and its diagnosis is highly dependent on subjective assessment. Electroencephalogram (EEG), as a non-invasive and economical neurophysiological tool, has garnered considerable attention in recent years in the research of auxiliary diagnosis and clinical application. However, there exists a limited number of articles that summarize this body of research. This study aims to investigate the current trends, emerging topics, and potential advancements in EEG research related to depression while also predicting the challenges that may arise within this field.
Methods:
We retrieved the literature related to depression and EEG published from April 16, 2005 to April 16, 2025 in Web of Science (WoSCC) and PubMed, and conducted data analysis and visual display using CiteSpace, VOS viewer, Bibliometrix, Scimago Graphica, Microsoft Excel 2021, and R software version 4.2.3.
Results:
From 2005 to 2025, 215 journals from 189 countries published papers in this field. The majority of the papers were published in Journal of Affective Disorders, and the average citation per paper was the highest in Biomedical Signal Processing and Control. China contributed the most publications, but the United States had the highest citation per paper. In terms of the total number of publications, Lanzhou University contributed the most papers. The top 5 keywords were major depression, alpha asymmetry, brain, asymmetry, and anxiety. Cluster analysis indicated that the research in this field is transforming from basic electrophysiological features to clinical applications, that is, exploring the significance of EEG in the diagnosis, classification, and prediction of depression.
Conclusion:
EEG research on depression is developing toward individualization and intelligence. In the future, efforts should be focused on standardizing processes, integrating multiple modalities, and clinical application to enhance its value in diagnosis and prognosis.
