Show simple item record

dc.contributor.authorBrowne, Matthewen_US
dc.contributor.authorCutmore, Timothyen_US
dc.contributor.editorDr. P.M. Rossini, Dr. M. Halletten_US
dc.date.accessioned2017-04-24T09:10:59Z
dc.date.available2017-04-24T09:10:59Z
dc.date.issued2002en_US
dc.identifier.issn13882457en_US
dc.identifier.doi10.1016/S1388-2457(02)00194-3en_US
dc.identifier.urihttp://hdl.handle.net/10072/6815
dc.description.abstractObjectives: The aim of this paper is to introduce and test a general, wavelet-based method for the automatic removal of noise and artefact from psychophysiological data. Methods: Statistical wavelet thresholding (SWT) performs blind source separation by transforming data to the wavelet domain, and subsequent filtering of wavelet coefficients based on a statistical framework. The observed wavelet coefficients are modelled using a Gaussian distribution, from which low-probability outliers are attenuated based on their z-scores. Results: The technique was applied to both simulated and real event-related potentials (ERP) data. SWT applied to artificial data displayed increased signal-to-noise ratio (SNR) improvements as noise amplitude increased. ERP averages of filtered experimental data displayed a correlation of 0.93 with operator-filtered data, compared with a correlation of 0.56 for unfiltered data. The energy of operator-designated contaminated trials was attenuated by a factor of 7.46 relative to uncontaminated trials. SNR improvement was observed in simulated tests. Conclusions: Variations of SWT may be useful in situations where one wishes to separate uncommon/uncharacteristic structures from time series data sets. For artefact removal applications, SWT appears to be a valid alternative to expert operator screening.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.publisher.placeIrelanden_US
dc.relation.ispartofpagefrom1403en_US
dc.relation.ispartofpageto1411en_US
dc.relation.ispartofjournalClinical Neurophysiologyen_US
dc.relation.ispartofvolume113en_US
dc.subject.fieldofresearchcode380304en_US
dc.subject.fieldofresearchcode380103en_US
dc.titleLow-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoisingen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Environmenten_US
gro.date.issued2015-05-04T22:04:36Z
gro.hasfulltextNo Full Text


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record