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Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Wang, Dongcuia; b | Mo, Fongminga | Zhang, Yangdea | Yang, Chaoc | Liu, Jund | Chen, Zhenchengb; * | Zhao, Jinfenga; *

Affiliations: [a] Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha 410008, China | [b] School of Life and Environmental Science, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin 541004, China | [c] School of Electronic Engineering and Automation, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin 541004, China | [d] The Social Welfare Hospital of Guilin, No. 89 North Zhongshan Road, Guilin 541001, China

Correspondence: [*] Address for correspondence: Zhencheng Chen, School of Life and Environmental Science, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin, China. Tel.: +86 15295885965; Fax: +86 0773-2292232; E-mail: [email protected]. Jinfeng Zhao, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, China. Tel.: +86 15295885965; Fax: +86 0773-2292232; E-mail: [email protected].

Keywords: Emotiv system, major depressive disorder, event-related potential

DOI: 10.3233/BME-151385

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S917-S923, 2015

Published: 2015
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In a previous study (unpublished), Emotiv headset was validated for capturing event-related potentials (ERPs) from normal subjects. In the present follow-up study, the signal quality of Emotiv headset was tested by the accuracy rate of discriminating Major Depressive Disorder (MDD) patients from the normal subjects. ERPs of 22 MDD patients and 15 normal subjects were induced by an auditory oddball task and the amplitude of N1, N2 and P3 of ERP components were specifically analyzed. The features of ERPs were statistically investigated. It is found that Emotiv headset is capable of discriminating the abnormal N1, N2 and P3 components in MDD patients. Relief-F algorithm was applied to all features for feature selection. The selected features were then input to a linear discriminant analysis (LDA) classifier with leave-one-out cross-validation to characterize the ERP features of MDD. 127 possible combinations out of the selected 7 ERP features were classified using LDA. The best classification accuracy was achieved to be 89.66%. These results suggest that MDD patients are identifiable from normal subjects by ERPs measured by Emotiv headset.

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