Machine learning approaches for diagnosing depression using EEG: A review

Abstract: Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Machine learning approaches for diagnosing depression using EEG: A review ; volume:13 ; number:1 ; year:2022 ; pages:224-235 ; extent:12 ; Translational Neuroscience ; 13, Heft 1 (2022), 224-235 (gesamt 12)

Contributors, Places and Time

• Creator: Liu, Yuan ; Pu, Changqin ; Xia, Shan ; Deng, Dingyu ; Wang, Xing ; Li, Mengqian

Further information

• DOI: 10.1515/tnsci-2022-0234
• URN: urn:nbn:de:101:1-2022081314025720041606
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:28 AM CEST