Show simple item record

dc.contributor.authorREILLY, RICHARDen
dc.contributor.authorNOLAN, HUGHen
dc.contributor.authorNOLAN, HUGHen
dc.contributor.authorWHELAN, ROBERTen
dc.date.accessioned2010-10-17T16:44:05Z
dc.date.available2010-10-17T16:44:05Z
dc.date.issued2010en
dc.date.submitted2010en
dc.identifier.citationNolan H, Whelan R, Reilly R.B, FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection, Journal of Neuroscience Methods, 192, 1, 2010, 152-162en
dc.identifier.otherYen
dc.identifier.urihttp://hdl.handle.net/2262/41103
dc.descriptionPUBLISHEDen
dc.description.abstractElectroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time-series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n=47) and real EEG (n=47) data on 128-, 64-, and 32-scalp electrode arrays. Faster was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had > 90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise. FASTER generally had > 60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach.en
dc.description.sponsorshipThis study was partly funded by an Enterprise Ireland grant to R.B. Reilly (eBiomed: eHealthCare based on Biomedical Signal Processing and ICT for Integrated Diagnosis and Treatment of Disease), an Irish Research Council for Science Engineering and Technology Postgraduate scholarship to H. Nolan, and by Science Foundation Ireland (09 / RFP / NE2382).en
dc.format.extent152-162en
dc.language.isoenen
dc.relation.ispartofseriesJournal of Neuroscience Methodsen
dc.relation.ispartofseries192en
dc.relation.ispartofseries1en
dc.rightsYen
dc.subjectNeuroscienceen
dc.subjectElectroencephalographyen
dc.titleFASTER: Fully Automated Statistical Thresholding for EEG artifact Rejectionen
dc.typeJournal Articleen
dc.contributor.sponsorIrish Research Council for Science and Engineering Technology (IRCSET)en
dc.contributor.sponsorEnterprise Irelanden
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/reillyrien
dc.identifier.peoplefinderurlhttp://people.tcd.ie/whelanr3en
dc.identifier.peoplefinderurlhttp://people.tcd.ie/nolanh4en
dc.identifier.rssinternalid68277en
dc.identifier.doihttp://dx.doi.org/10.1016/j.jneumeth.2010.07.015en
dc.subject.TCDThemeNeuroscienceen
dc.subject.TCDThemeNext Generation Medical Devicesen
dc.subject.TCDTagEEGen
dc.subject.TCDTagEEG ANALYSISen
dc.subject.TCDTagQUANTITATIVE EEG ANALYSISen
dc.identifier.rssurihttp://dx.doi.org/10.1016/j.jneumeth.2010.07.015en
dc.identifier.orcid_id0000-0001-8578-1245en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record