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

DSpace/Manakin Repository

Search OPUS


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Skinner, BT
dc.contributor.author Nguyen, HT
dc.contributor.author Liu, DK
dc.date.accessioned 2009-11-09T05:36:37Z
dc.date.issued 2007
dc.identifier.citation Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2007, pp. 3120 - 3123
dc.identifier.issn 1557-170X
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 genetic-based 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.
dc.relation.hasversion Accepted manuscript version
dc.title Classification of EEG signals using a genetic-based machine learning classifier.
dc.type Conference Proceeding
dc.parent Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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 Biomedical Instrumentation
dc.personcode 840115
dc.personcode 000350
dc.personcode 995215
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
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
pubs.consider-herdc true
utslib.collection.history General (ID: 2)


Files in this item

This item appears in the following Collection(s)

Show simple item record