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Article: Comparison of blind source separation methods in fast somatosensory-evoked potential detection

TitleComparison of blind source separation methods in fast somatosensory-evoked potential detection
Authors
KeywordsBlind source separation
Signal processing
Somatosensory-evoked potentials
Issue Date2011
PublisherLippincott Williams & Wilkins. The Journal's web site is located at http://www.clinicalneurophys.com
Citation
Journal Of Clinical Neurophysiology, 2011, v. 28 n. 2, p. 170-177 How to Cite?
AbstractBlind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. The algorithms we compared included second-order blind identification, estimation of signal parameters via rotation invariance technique, algorithm for multiple unknown signals extraction, joint approximate diagonalization of eigenmatrices, extended infomax, and fast independent component analysis. The performances of these BSS algorithms were determined by the correlation coefficients between the true and the extracted SEP signals. There were significant differences in the performances of the various BSS algorithms in a simulation study. In summary, second-order blind identification using six covariance matrix denoting SOBI6 was recommended as the most appropriate BSS method for fast SEP extraction from noisy backgrounds. Copyright © 2011 by the American Clinical Neurophysiology Society.
Persistent Identifierhttp://hdl.handle.net/10722/135314
ISSN
2015 Impact Factor: 1.337
2015 SCImago Journal Rankings: 0.578
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
Hong Kong Research Grants CouncilGRF HKU 7130/06E
Funding Information:

Supported in part by Hong Kong Research Grants Council Competitive Earmarked Research Grants (GRF HKU 7130/06E) and The University of Hong Kong CRCG Funds.

References

 

DC FieldValueLanguage
dc.contributor.authorLiu, Hen_HK
dc.contributor.authorChang, CQen_HK
dc.contributor.authorLuk, KDKen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2011-07-27T01:33:17Z-
dc.date.available2011-07-27T01:33:17Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Clinical Neurophysiology, 2011, v. 28 n. 2, p. 170-177en_HK
dc.identifier.issn0736-0258en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135314-
dc.description.abstractBlind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. The algorithms we compared included second-order blind identification, estimation of signal parameters via rotation invariance technique, algorithm for multiple unknown signals extraction, joint approximate diagonalization of eigenmatrices, extended infomax, and fast independent component analysis. The performances of these BSS algorithms were determined by the correlation coefficients between the true and the extracted SEP signals. There were significant differences in the performances of the various BSS algorithms in a simulation study. In summary, second-order blind identification using six covariance matrix denoting SOBI6 was recommended as the most appropriate BSS method for fast SEP extraction from noisy backgrounds. Copyright © 2011 by the American Clinical Neurophysiology Society.en_HK
dc.languageengen_US
dc.publisherLippincott Williams & Wilkins. The Journal's web site is located at http://www.clinicalneurophys.comen_HK
dc.relation.ispartofJournal of Clinical Neurophysiologyen_HK
dc.rightsThis is a non-final version of an article published in final form in Journal of Clinical Neurophysiology, 2011, v. 28 n. 2, p. 170-177-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBlind source separationen_HK
dc.subjectSignal processingen_HK
dc.subjectSomatosensory-evoked potentialsen_HK
dc.subject.meshAlgorithms-
dc.subject.meshElectric Stimulation-
dc.subject.meshElectroencephalography-
dc.subject.meshEvoked Potentials, Somatosensory-
dc.subject.meshMonitoring, Intraoperative - methods-
dc.titleComparison of blind source separation methods in fast somatosensory-evoked potential detectionen_HK
dc.typeArticleen_HK
dc.identifier.emailChang, CQ: cqchang@eee.hku.hken_HK
dc.identifier.emailLuk, KDK: hcm21000@hku.hken_HK
dc.identifier.emailHu, Y: yhud@hku.hken_HK
dc.identifier.authorityChang, CQ=rp00095en_HK
dc.identifier.authorityLuk, KDK=rp00333en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1097/WNP.0b013e31821213bden_HK
dc.identifier.pmid21399512-
dc.identifier.scopuseid_2-s2.0-79955054423en_HK
dc.identifier.hkuros189058en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79955054423&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume28en_HK
dc.identifier.issue2en_HK
dc.identifier.spage170en_HK
dc.identifier.epage177en_HK
dc.identifier.isiWOS:000289068000009-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLiu, H=35976455700en_HK
dc.identifier.scopusauthoridChang, CQ=7407033052en_HK
dc.identifier.scopusauthoridLuk, KDK=7201921573en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK

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