2.50
Hdl Handle:
http://hdl.handle.net/10547/270594
Title:
A Bayesian model averaging methodology for detecting EEG artifacts
Authors:
Schetinin, Vitaly ( 0000-0003-1826-0153 ) ; Maple, Carsten
Abstract:
In this paper we describe a Bayesian Model Averaging (BMA) methodology developed for detecting artifacts in electroencephalograms (EEGs). The EEGs can be heavily corrupted by cardiac, eye movement, muscle and noise artifacts, so that EEG experts need to automatically detect them with a given level of confidence. In theory, the BMA methodology allows experts to evaluate the confidence in decision making most accurately. However, the non- stationary nature of EEGs makes the use of this methodology difficult. In our experiments with the sleep EEGs, the proposed BMA technique is shown to provide a better performance in terms of predictive accuracy.
Citation:
Schetinin, V.; Maple, C.; (2007) A Bayesian Model Averaging Methodology for Detecting EEG Artifacts, Digital Signal Processing, 15th International Conference on , pp.499-502
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date:
2007
URI:
http://hdl.handle.net/10547/270594
DOI:
10.1109/ICDSP.2007.4288628
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4288628
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
1424408822
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorSchetinin, Vitalyen_GB
dc.contributor.authorMaple, Carstenen_GB
dc.date.accessioned2013-02-27T15:54:19Z-
dc.date.available2013-02-27T15:54:19Z-
dc.date.issued2007-
dc.identifier.citationSchetinin, V.; Maple, C.; (2007) A Bayesian Model Averaging Methodology for Detecting EEG Artifacts, Digital Signal Processing, 15th International Conference on , pp.499-502en_GB
dc.identifier.isbn1424408822-
dc.identifier.doi10.1109/ICDSP.2007.4288628-
dc.identifier.urihttp://hdl.handle.net/10547/270594-
dc.description.abstractIn this paper we describe a Bayesian Model Averaging (BMA) methodology developed for detecting artifacts in electroencephalograms (EEGs). The EEGs can be heavily corrupted by cardiac, eye movement, muscle and noise artifacts, so that EEG experts need to automatically detect them with a given level of confidence. In theory, the BMA methodology allows experts to evaluate the confidence in decision making most accurately. However, the non- stationary nature of EEGs makes the use of this methodology difficult. In our experiments with the sleep EEGs, the proposed BMA technique is shown to provide a better performance in terms of predictive accuracy.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4288628en_GB
dc.subjectartifact detectionen_GB
dc.subjectelectroencephalogramen_GB
dc.subjectmachine learningen_GB
dc.subjectuncertainty estimationen_GB
dc.titleA Bayesian model averaging methodology for detecting EEG artifactsen
dc.typeConference papers, meetings and proceedingsen
All Items in UOBREP are protected by copyright, with all rights reserved, unless otherwise indicated.