Classification of EEG signals using a genetic-based machine learning classifier

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dc.contributor.author Nguyen, Hung en_US
dc.contributor.author Liu, Dikai en_US
dc.contributor.author Skinner, Brad en_US
dc.contributor.editor Dittmar, A; Clark, J en_US
dc.date.accessioned 2009-11-09T05:36:37Z
dc.date.available 2009-11-09T05:36:37Z
dc.date.issued 2007 en_US
dc.identifier 2006009449 en_US
dc.identifier.citation Skinner Bradley, Nguyen Hung, and Liu Dikai 2007, 'Classification of EEG signals using a genetic-based machine learning classifier', IEEE, Lyon, France, pp. 3120-3123. en_US
dc.identifier.issn 1-4244-0788-5 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/2769
dc.description.abstract This paper investigates the efficacy of the geneticbased learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. en_US
dc.publisher IEEE en_US
dc.relation.hasversion Accepted manuscript version
dc.relation.isbasedon http://dx.doi.org/10.1109/IEMBS.2007.4352990 en_US
dc.title Classification of EEG signals using a genetic-based machine learning classifier en_US
dc.parent Proceedings of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Lyon, France en_US
dc.identifier.startpage 3120 en_US
dc.identifier.endpage 3123 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference.location Lyon, France en_US
dc.for 090303 en_US
dc.personcode 995215 en_US
dc.personcode 840115 en_US
dc.personcode 000350 en_US
dc.percentage 100 en_US
dc.classification.name Biomedical Instrumentation en_US
dc.classification.type FOR-08 en_US
dc.custom IEEE Engineering in Medicine and Biology Society Annual Conference en_US
dc.date.activity 20070823 en_US
dc.location.activity Lyon, France en_US
dc.description.keywords learning classifier systems (LCSs), XCS, evolutionary computation, genetic-based machine learning (GBML), electroencephalogram en_US
dc.staffid 000350 en_US


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