An EEG based pervasive depression detection for females

Authors:
Xiaowei Zhang;Bin Hu;Lin Zhou;Philip Moore;Jing Chen
Affiliations:
The School of Information Science and Engineering, Lanzhou University, Lanzhou, China;The School of Information Science and Engineering, Lanzhou University, Lanzhou, China;The School of Information Science and Engineering, Lanzhou University, Lanzhou, China;The School of Computing, Telecommunications and Networks, Birmingham City University, Birmingham, UK;The School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Venue:
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Year:
2012

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Abstract

Recently, depression detection is mainly completed by some rating scales. This procedure requires attendance of physicians and the results may be more subjective. To meet emergent needs of objective and pervasive depression detection, we propose an EEG based approach for females. In the experiment, EEG of 13 depressed females and 12 age matched controls were collected in a resting state with eyes closed. Linear and nonlinear features extracted from artifact-free EEG epochs were subjected to statistical analysis to examine the significance of differences. Results showed that differences were significant for some EEG features between two groups (p