Tensor decomposition for EEG signals retrieval

Cao, Z; Chang, YC; Prasad, M; Tanveer, M; Lin, CT

Tensor decomposition for EEG signals retrieval

Cao, Z Chang, YC Prasad, M

Tanveer, M Lin, CT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019, 2019-October, pp. 2423-2427
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Cao, Z
dc.contributor.author	Chang, YC
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.contributor.author	Tanveer, M
dc.contributor.author	Lin, CT
dc.date	2019-10-06
dc.date.accessioned	2020-05-06T02:18:42Z
dc.date.available	2020-05-06T02:18:42Z
dc.date.issued	2019-10-01
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019, 2019-October, pp. 2423-2427
dc.identifier.isbn	9781728145693
dc.identifier.issn	1062-922X
dc.identifier.uri	http://hdl.handle.net/10453/140502
dc.description.abstract	© 2019 IEEE. Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
dc.relation.ispartof	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
dc.relation.isbasedon	10.1109/SMC.2019.8914076
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Tensor decomposition for EEG signals retrieval
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-10-01T00:00:00+1000Z
dc.date.updated	2020-05-06T02:18:40Z
pubs.finish-date	2019-10-09
pubs.publication-status	Published
pubs.start-date	2019-10-06
pubs.volume	2019-October
utslib.start-page	2423

Abstract:

© 2019 IEEE. Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.

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http://hdl.handle.net/10453/140502