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Volume 2018 (2018), Article ID 5238028, 13 pages
Research Article

A Pervasive Approach to EEG-Based Depression Detection

1Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
2CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
3Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
4Department of Child Psychology, Lanzhou University Second Hospital, Lanzhou, China
5Beijing Anding Hospital, Capital Medical University, Beijing, China
6The Third People’s Hospital of Tianshui City, Tianshui, China
7Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
8Computer Systems Institute, ETH Zürich, Zürich, Switzerland

Correspondence should be addressed to Bin Hu; nc.ude.uzl@hb

Received 31 March 2017; Revised 17 November 2017; Accepted 4 January 2018; Published 6 February 2018

Academic Editor: Haiying Wang

Copyright © 2018 Hanshu Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.