Adaptive EEG thought pattern classifier for advanced wheelchair control

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dc.contributor.author Craig, DA
dc.contributor.author Nguyen, HT
dc.contributor.editor Dittmar, A
dc.contributor.editor Clark, J
dc.date.accessioned 2009-11-09T05:36:36Z
dc.date.issued 2007-01
dc.identifier.citation Proceedings of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 2544 - 2547
dc.identifier.isbn 1-4244-0788-5
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/2765
dc.description.abstract This paper presents a real-time Electroencephalogram (EEG) classification system, with the goal of enhancing the control of a head-movement controlled power wheelchair for patients with chronic Spinal Cord Injury (SCI). Using a 32 channel recording device, mental command data was collected from 10 participants. This data was used to classify three different mental commands, to supplement the five commands already available using head movement control. Of the 32 channels that were recorded only 4 were used in the classification, achieving an average classification rate of 82%. This paper also demonstrates that there is an advantage to be gained by doing adaptive training of the classifier. That is, customizing the classifier to a person previously unseen by the classifier caused their average recognition rates to improve from 52.5% up to 77.5%.
dc.publisher IEEE
dc.relation.isbasedon 10.1109/IEMBS.2007.4352847
dc.subject EEG Classifier, wheelchair control, thought pattern
dc.subject EEG Classifier, wheelchair control, thought pattern
dc.title Adaptive EEG thought pattern classifier for advanced wheelchair control
dc.type Conference Proceeding
dc.parent Proceedings of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society
dc.journal.number en_US
dc.publocation Lyon, France en_US
dc.publocation Lyon, France
dc.identifier.startpage 2544 en_US
dc.identifier.endpage 2547 en_US
dc.cauo.name FEIT. A/DRsch Ctre for Health Technologies en_US
dc.conference Verified OK en_US
dc.conference IEEE Engineering in Medicine and Biology Society Annual Conference
dc.conference.location Lyon, France en_US
dc.for 0903 Biomedical Engineering
dc.personcode 930162 en_US
dc.personcode 840115 en_US
dc.percentage 100 en_US
dc.classification.name Biomedical Engineering 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.date.activity 2007-08-23
dc.location.activity Lyon, France en_US
dc.location.activity Lyon, France
dc.description.keywords EEG Classifier, wheelchair control, thought pattern en_US
dc.description.keywords EEG Classifier, wheelchair control, thought pattern
dc.staffid 840115 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/Strength - Health Technologies


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