SYSTEMATIC REVIEW article
Front. Neurosci.
Sec. Translational Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1653693
This article is part of the Research TopicApplications of Intelligent Sensing and Biomedical Information Processing in Clinical NeuroscienceView all articles
The Application of Electroencephalogram in Depression Research: Bibliometric and Technological Application Analysis from 2005 to 2025
Provisionally accepted
- 1Shandong University of Traditional Chinese Medicine, Jinan, China
- 2Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
- 3Handan Maternal and Child Health Hospital, Handan, China
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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.: 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.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.
Keywords: Electroencephalogram, Depression, diagnosis, Bibliometrics, visual analysis convolutional neural networks LSTM: long short-term memory SVM: support vector machines RF: random forests k-NN: k-nearest neighbors LOSO: leave-one-subject-out XAI: explainable AI LRP: Layer-wise Relevance Propagation
Received: 25 Jun 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Hao, Han, Huang, Hao, Yu, Wei and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jian Huang, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
Cangcang Hao, Handan Maternal and Child Health Hospital, Handan, China
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