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An EEG-Based Depression Detection Method Using Machine Learning Model

Ran Bai1, Yu Guo2, Xianwu Tan1, Lei Feng3,4,5, and Haiyong Xie 5,1
1. National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (NEL-PSRPC), Beijing, China
2. Beijing University of Posts and Telecommunications, Beijing, China
3. The National Clinical Research Center for Mental Disorders, Beijing, China
4. Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing China
5. Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
Abstract—Depression, different from usual mood fluctuations and short-lived emotional responses to challenges in everyday life, is a common illness worldwide, with more than 300 million people affected. Although there are known, effective treatments for depression, fewer than half of those affected in the world (in many countries, fewer than 10%) receive such treatments. The diagnose of depression is usually subject to doctors due to the lack of biomarkers of depression. Electroencephalogram (EEG) is an easy-to-use, cost-effective technique that records electrical activity in brain. In this study, 64-channel EEG data was collected from 213 subjects including 71 health controls and 142 depression patients. 13 different features were extracted from EEG signals from all 7 sub-bands of all channels. 3 different feature selection models were used to find the subset of features that best represents the characteristics of EEG signal and 6 machine learning models were applied on all subsets of features to find the model that gained the highest accuracy and recall on depression detection.

Index Terms—depression, MDD, EEG, machine learning

Cite: Ran Bai, Yu Guo, Xianwu Tan, Lei Feng, and Haiyong Xie, "An EEG-Based Depression Detection Method Using Machine Learning Model," International Journal of Pharma Medicine and Biological Sciences, Vol. 10, No. 1, pp. 17-22, January 2021. doi: 10.18178/ijpmbs.10.1.17-22

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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