Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression

• Published in: IEEE access Vol. 7; pp. 92630 - 92641
• Main Authors: Peng, Hong, Xia, Chen, Wang, Zihan, Zhu, Jing, Zhang, Xin, Sun, Shuting, Li, Jianxiu, Huo, Xiaoning, Li, Xiaowei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomarkers; Brain; Classifiers; Connectivity analysis; Correlation coefficients; Depression; EEG; Electroencephalography; Frequencies; functional connectivity; Functional magnetic resonance imaging; Indexes; Mental depression; multivariate pattern analysis; Oscillators; Pattern analysis; Permutations; Phase lag; resting-state; Support vector machines

Resting-state electroencephalography (EEG) studies have shown significant group differences in functional connectivity networks between patients with depression and healthy controls. The present study aims to identify the altered EEG resting-state functional connectivity patterns of depressed patien...