Wang P, Dai AL, Guo XR, Jiang HT. Portable electroencephalography in early detection of depression: Progress and future directions. World J Psychiatry 2025; 15(8): 107725 [DOI: 10.5498/wjp.v15.i8.107725]
Corresponding Author of This Article
Hai-Teng Jiang, Assistant Professor, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, No. 866 Yuhangtang Road, Hangzhou 310058, Zhejiang Province, China. h.jiang@zju.edu.cn
Research Domain of This Article
Psychiatry
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Aug 19, 2025; 15(8): 107725 Published online Aug 19, 2025. doi: 10.5498/wjp.v15.i8.107725
Portable electroencephalography in early detection of depression: Progress and future directions
Pan Wang, An-Lu Dai, Xuan-Ru Guo, Hai-Teng Jiang
Pan Wang, An-Lu Dai, Department of Psychiatry and Mental Health, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
Xuan-Ru Guo, Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
Xuan-Ru Guo, Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou 311121, Zhejiang Province, China
Hai-Teng Jiang, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang Province, China
Hai-Teng Jiang, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang Province, China
Co-first authors: Pan Wang and An-Lu Dai.
Author contributions: Wang P and Dai AL contributed to drafting and table preparation and made equal contributions to this manuscript as co-first authors. Dai AL performed data collection and contributed to writing; Guo XR provided critical suggestions and reviewed the manuscript for important intellectual content; Jiang HT designed the outline and coordinated the writing of the paper.
Supported by Ministry of Science and Technology of the People’s Republic of China-Major Projects, No. 2022ZD0212400; and National Natural Science Foundation of China, No. 82371453.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hai-Teng Jiang, Assistant Professor, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, No. 866 Yuhangtang Road, Hangzhou 310058, Zhejiang Province, China. h.jiang@zju.edu.cn
Received: April 1, 2025 Revised: April 22, 2025 Accepted: June 12, 2025 Published online: August 19, 2025 Processing time: 132 Days and 18.5 Hours
Abstract
Traditional diagnostic tools for depression, such as the Patient Health Questionnaire-9, are susceptible to subjective bias, increasing the risk of misdiagnosis and emphasizing the critical need for objective biomarkers. This minireview evaluates the emerging role of portable electroencephalography (EEG) as a cost-effective, accessible solution for early depression detection. By synthesizing findings from 45 studies (selected from 764 screened articles), we highlight EEG’s capacity to identify aberrant neural oscillations associated with core depressive symptoms, including anhedonia, excessive guilt, and persistent low mood. Advances in portable systems demonstrate promising classification accuracy when integrated with machine learning algorithms, with long short-term memory models achieving > 90% accuracy in recent trials. However, persistent challenges, such as signal quality variability, motion artifacts, and limited clinical validation, hinder widespread adoption. Further innovation in sensor optimization, multimodal data integration, and real-world clinical trials is essential to translate portable EEG into a reliable diagnostic tool. This minireview underscores the transformative potential of neurotechnology in psychiatry while advocating for rigorous standardization to bridge the gap between research and clinical practice.
Core Tip: The application of conventional electroencephalography (EEG) in depression screening is limited by high equipment cost, operational complexity, and low clinical applicability. This minireview highlights the current research status of portable EEG in depression screening. Systematic analysis of 45 full-text studies shows portable EEG has significant potential in feature extraction (frequency domain, time domain, time-frequency analysis, nonlinear features, and functional connectivity) and prediction capability at the single-subject level when combined with machine learning. Future studies should focus on algorithm optimization, improving data quality, and promoting clinical implementation of portable EEG for depression screening.
