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dc.contributor.authorCutmore, Timothyen_US
dc.contributor.authorCelka, Patricken_US
dc.date.accessioned2017-05-03T12:13:12Z
dc.date.available2017-05-03T12:13:12Z
dc.date.issued2008en_US
dc.date.modified2012-09-06T22:10:39Z
dc.identifier.issn02698803en_US
dc.identifier.urihttp://hdl.handle.net/10072/22110
dc.description.abstractThis paper used three theoretically different algorithms for reducing noise in event-related potential (ERP) data. It examined the possibility that a hybrid of these methods could show gains in noise reduction beyond that obtained with any single method. The well-known ERP oddball paradigm was used to evaluate three denoising methods: statistical wavelet transform (wavelet-Z), a smooth subspace wavelet filter (wavelet-S), and subspace PCA. The six possible orders of serial application of these methods to the oddball waveforms were compared for efficacy in signal enhancement. It was found that the order was not commutative, with the best results obtained from applying the wavelet-Z first. Comparison of oddball and frequent trials in the grand average and in individual averages showed considerable enhancement of the differences. It was concluded that denoising to remove variance caused by rare sizeable artifacts is best done first, followed by state space PCA and a light-bias model-based wavelet denoising. The ability to detect and distinguish the effects of variables (such as task, drug effects, individual differences, etc.) on ERPs related to human cognition could be considerably advanced using the denoising methods described here.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherHogrefe & Huber Publishersen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://www.psycontent.com/content/x600401617n8t068/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom111en_US
dc.relation.ispartofpageto120en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalJournal of Psychophysiologyen_US
dc.relation.ispartofvolume22en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchcode380103en_US
dc.subject.fieldofresearchcode280204en_US
dc.titleComposite noise reduction of ERPs using wavelet, model-based and principal component subspace methodsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Health, School of Applied Psychologyen_US
gro.date.issued2008
gro.hasfulltextNo Full Text


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