dc.contributor.author | REILLY, RICHARD | en |
dc.contributor.author | NOLAN, HUGH | en |
dc.contributor.author | NOLAN, HUGH | en |
dc.contributor.author | WHELAN, ROBERT | en |
dc.date.accessioned | 2010-10-17T16:44:05Z | |
dc.date.available | 2010-10-17T16:44:05Z | |
dc.date.issued | 2010 | en |
dc.date.submitted | 2010 | en |
dc.identifier.citation | Nolan H, Whelan R, Reilly R.B, FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection, Journal of Neuroscience Methods, 192, 1, 2010, 152-162 | en |
dc.identifier.other | Y | en |
dc.identifier.uri | http://hdl.handle.net/2262/41103 | |
dc.description | PUBLISHED | en |
dc.description.abstract | Electroencephalogram (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.sponsorship | This 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.extent | 152-162 | en |
dc.language.iso | en | en |
dc.relation.ispartofseries | Journal of Neuroscience Methods | en |
dc.relation.ispartofseries | 192 | en |
dc.relation.ispartofseries | 1 | en |
dc.rights | Y | en |
dc.subject | Neuroscience | en |
dc.subject | Electroencephalography | en |
dc.title | FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/reillyri | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/whelanr3 | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/nolanh4 | en |
dc.identifier.rssinternalid | 68277 | en |
dc.identifier.doi | http://dx.doi.org/10.1016/j.jneumeth.2010.07.015 | en |
dc.subject.TCDTheme | Neuroscience | en |
dc.subject.TCDTheme | Next Generation Medical Devices | en |
dc.subject.TCDTag | EEG | en |
dc.subject.TCDTag | EEG ANALYSIS | en |
dc.subject.TCDTag | QUANTITATIVE EEG ANALYSIS | en |
dc.identifier.rssuri | http://dx.doi.org/10.1016/j.jneumeth.2010.07.015 | en |
dc.identifier.orcid_id | 0000-0001-8578-1245 | en |
dc.contributor.sponsor | Irish Research Council for Science and Engineering Technology (IRCSET) | en |
dc.contributor.sponsor | Enterprise Ireland | en |