Classification of wheelchair commands using brain computer interface: Comparison between able-bodied persons and patients with tetraplegia

Chai, R; Ling, SH; Hunter, GP; Tran, Y; Nguyen, HT

Classification of wheelchair commands using brain computer interface: Comparison between able-bodied persons and patients with tetraplegia

Chai, R

Ling, SH

Hunter, GP Tran, Y

Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, pp. 989 - 992
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Hunter, GP	en_US
dc.contributor.author	Tran, Y https://orcid.org/0000-0002-1741-4205	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, pp. 989 - 992	en_US
dc.identifier.isbn	9781457702167	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/49869
dc.description.abstract	This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubik's cube rolling forward; these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2013.6609669	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Quadriplegia	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Wheelchairs	en_US
dc.subject.mesh	Task Performance and Analysis	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Time Factors	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Aged, 80 and over	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Brain-Computer Interfaces	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Classification of wheelchair commands using brain computer interface: Comparison between able-bodied persons and patients with tetraplegia	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Osaka, Japan	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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	in_progress
pubs.publication-status	Published	en_US

Abstract:

This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubik's cube rolling forward; these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window. © 2013 IEEE.

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

http://hdl.handle.net/10453/23990