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Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

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 acti...

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Autores principales: Wang, Xiulin, Liu, Wenya, Wang, Xiaoyu, Mu, Zhen, Xu, Jing, Chang, Yi, Zhang, Qing, Wu, Jianlin, Cong, Fengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714749/
https://www.ncbi.nlm.nih.gov/pubmed/34975439
http://dx.doi.org/10.3389/fnhum.2021.799288
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author Wang, Xiulin
Liu, Wenya
Wang, Xiaoyu
Mu, Zhen
Xu, Jing
Chang, Yi
Zhang, Qing
Wu, Jianlin
Cong, Fengyu
author_facet Wang, Xiulin
Liu, Wenya
Wang, Xiaoyu
Mu, Zhen
Xu, Jing
Chang, Yi
Zhang, Qing
Wu, Jianlin
Cong, Fengyu
author_sort Wang, Xiulin
collection PubMed
description 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.
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spelling pubmed-87147492021-12-30 Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization Wang, Xiulin Liu, Wenya Wang, Xiaoyu Mu, Zhen Xu, Jing Chang, Yi Zhang, Qing Wu, Jianlin Cong, Fengyu Front Hum Neurosci Human Neuroscience 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. Frontiers Media S.A. 2021-12-15 /pmc/articles/PMC8714749/ /pubmed/34975439 http://dx.doi.org/10.3389/fnhum.2021.799288 Text en Copyright © 2021 Wang, Liu, Wang, Mu, Xu, Chang, Zhang, Wu and Cong. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Human Neuroscience
Wang, Xiulin
Liu, Wenya
Wang, Xiaoyu
Mu, Zhen
Xu, Jing
Chang, Yi
Zhang, Qing
Wu, Jianlin
Cong, Fengyu
Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
title Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
title_full Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
title_fullStr Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
title_full_unstemmed Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
title_short Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
title_sort shared and unshared feature extraction in major depression during music listening using constrained tensor factorization
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714749/
https://www.ncbi.nlm.nih.gov/pubmed/34975439
http://dx.doi.org/10.3389/fnhum.2021.799288
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