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Article: Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only

TitleAutomatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only
Authors
KeywordsAutomatic artifact correction
Blind source separation (BSS)
Electroencephalogram (EEG)
Event-related potential (ERP)
Higher order statistics (HOS)
Independent component analysis (ICA)
Second order statistics (SOS)
Issue Date2006
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/medengphy
Citation
Medical Engineering And Physics, 2006, v. 28 n. 8, p. 780-794 How to Cite?
AbstractEvent-related potentials (ERP) are in general masked by various kinds of artifacts. To attenuate the effects of artifacts, various schemes have been introduced, such as epoch rejection, electro-oculogram (EOG) regression and independent component analysis (ICA). However, none of the existing techniques can automatically remove various kinds of artifacts from a single ERP epoch. EOG regression cannot handle artifacts other than ocular ones. ICA incorporating higher order statistics (HOS) normally requires data with large number of time samples in order that the solution is robust. In this paper we blindly separate the multi-channel ERP into source components by estimating the correlation matrices of the data. Since only second order statistics (SOS) is involved, the process performs well at the single epoch level. Automatic artifact identification is performed in the source domain by introducing objective criteria for various artifacts. Criteria are based on time domain signal amplitude for blink and spurious peak artifact, scalp distribution of signal power for eye movement artifact and power distribution of frequency components for muscle artifact. The correction procedure can be completed by removing the identified artifactual sources from the raw multi-channel ERP. © 2005 IPEM.
Persistent Identifierhttp://hdl.handle.net/10722/155326
ISSN
2015 Impact Factor: 1.619
2015 SCImago Journal Rankings: 0.784
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTing, KHen_US
dc.contributor.authorFung, PCWen_US
dc.contributor.authorChang, CQen_US
dc.contributor.authorChan, FHYen_US
dc.date.accessioned2012-08-08T08:32:54Z-
dc.date.available2012-08-08T08:32:54Z-
dc.date.issued2006en_US
dc.identifier.citationMedical Engineering And Physics, 2006, v. 28 n. 8, p. 780-794en_US
dc.identifier.issn1350-4533en_US
dc.identifier.urihttp://hdl.handle.net/10722/155326-
dc.description.abstractEvent-related potentials (ERP) are in general masked by various kinds of artifacts. To attenuate the effects of artifacts, various schemes have been introduced, such as epoch rejection, electro-oculogram (EOG) regression and independent component analysis (ICA). However, none of the existing techniques can automatically remove various kinds of artifacts from a single ERP epoch. EOG regression cannot handle artifacts other than ocular ones. ICA incorporating higher order statistics (HOS) normally requires data with large number of time samples in order that the solution is robust. In this paper we blindly separate the multi-channel ERP into source components by estimating the correlation matrices of the data. Since only second order statistics (SOS) is involved, the process performs well at the single epoch level. Automatic artifact identification is performed in the source domain by introducing objective criteria for various artifacts. Criteria are based on time domain signal amplitude for blink and spurious peak artifact, scalp distribution of signal power for eye movement artifact and power distribution of frequency components for muscle artifact. The correction procedure can be completed by removing the identified artifactual sources from the raw multi-channel ERP. © 2005 IPEM.en_US
dc.languageengen_US
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/medengphyen_US
dc.relation.ispartofMedical Engineering and Physicsen_US
dc.rightsMedical Engineering & Physics. Copyright © Elsevier Ltd.-
dc.subjectAutomatic artifact correction-
dc.subjectBlind source separation (BSS)-
dc.subjectElectroencephalogram (EEG)-
dc.subjectEvent-related potential (ERP)-
dc.subjectHigher order statistics (HOS)-
dc.subjectIndependent component analysis (ICA)-
dc.subjectSecond order statistics (SOS)-
dc.subject.meshAlgorithmsen_US
dc.subject.meshArtifactsen_US
dc.subject.meshArtificial Intelligenceen_US
dc.subject.meshComputer Simulationen_US
dc.subject.meshDiagnosis, Computer-Assisted - Methodsen_US
dc.subject.meshElectroencephalography - Methodsen_US
dc.subject.meshEvoked Potentials, Visual - Physiologyen_US
dc.subject.meshHumansen_US
dc.subject.meshModels, Neurologicalen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshPattern Recognition, Automated - Methodsen_US
dc.subject.meshReproducibility Of Resultsen_US
dc.subject.meshSensitivity And Specificityen_US
dc.subject.meshVisual Cortex - Physiologyen_US
dc.titleAutomatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics onlyen_US
dc.typeArticleen_US
dc.identifier.emailChang, CQ:cqchang@eee.hku.hken_US
dc.identifier.authorityChang, CQ=rp00095en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.medengphy.2005.11.006en_US
dc.identifier.pmid16406675-
dc.identifier.scopuseid_2-s2.0-33744979251en_US
dc.identifier.hkuros131488-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33744979251&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue8en_US
dc.identifier.spage780en_US
dc.identifier.epage794en_US
dc.identifier.isiWOS:000240693800004-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridTing, KH=7101844691en_US
dc.identifier.scopusauthoridFung, PCW=7101613315en_US
dc.identifier.scopusauthoridChang, CQ=7407033052en_US
dc.identifier.scopusauthoridChan, FHY=7202586429en_US

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