Adaptive EEG thought pattern classifier for advanced wheelchair control

Craig, DA; Nguyen, HT

Adaptive EEG thought pattern classifier for advanced wheelchair control

Craig, DA Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2007, pp. 2544 - 2547
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Craig, DA	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2007, pp. 2544 - 2547	en_US
dc.identifier.isbn	1424407885	en_US
dc.identifier.isbn	9781424407880	en_US
dc.identifier.issn	0589-1019	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%. © 2007 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0666942
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en_US
dc.relation.isbasedon	10.1109/IEMBS.2007.4352847	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Spinal Cord Injuries	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Wheelchairs	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	User-Computer Interface	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Adaptive EEG thought pattern classifier for advanced wheelchair control	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Lyon, France	en_US
pubs.embargo.period	Not known	en_US
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 - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

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%. © 2007 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/2765