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Shared and Unshared Feature Extraction in Major Depression during Music Listening using Constrained Tensor Factorization

Provisionally accepted
The final version of the article will be published here soon pending final quality checks
 Xiulin Wang1, 2*,  Wenya Liu2, 3,  Jing Xu4,  Yi Chang4, Qing Zhang1,  Jianlin Wu1 and  Fengyu Cong2, 3, 5, 6
  • 1Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, China
  • 2School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China
  • 3Faculty of Information Technology, University of Jyväskylä, Finland
  • 4Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, China
  • 5School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China
  • 6Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province, Dalian University of Technology, China

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

Keywords: Candecomp/Parafac, constrained tensor factorization, EEG, Major Depressive Disorder, naturalistic music stimuli

Received: 21 Oct 2021; Accepted: 22 Nov 2021.

Copyright: © 2021 Wang, Liu, Xu, Chang, Zhang, Wu and Cong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Xiulin Wang, Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China