Discrimination of left and right leg motor imagery for brain-computer interfaces

Boord, P; Craig, A; Tran, Y; Nguyen, H

Discrimination of left and right leg motor imagery for brain-computer interfaces

Boord, P Craig, A

Tran, Y

Nguyen, H

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Publication Type:: Journal Article
Citation:: Medical and Biological Engineering and Computing, 2010, 48 (4), pp. 343 - 350
Issue Date:: 2010-04-01

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Field	Value	Language
dc.contributor.author	Boord, P	en_US
dc.contributor.author	Craig, A https://orcid.org/0000-0001-7647-7604	en_US
dc.contributor.author	Tran, Y https://orcid.org/0000-0002-1741-4205	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.date.available	2010-01-07	en_US
dc.date.issued	2010-04-01	en_US
dc.identifier.citation	Medical and Biological Engineering and Computing, 2010, 48 (4), pp. 343 - 350	en_US
dc.identifier.issn	0140-0118	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13521
dc.description.abstract	This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The results demonstrate that MMI of the left and right leg produce a contralateral preponderance of EEG alpha band desynchronization, which can be spatially discriminated. This suggests that lower extremity MMI could provide signals for additional BCI channels. The study also shows that movement imitation enhances alpha band desynchronization during MMI, and might provide a useful aid in the identification and training of BCI signals. © International Federation for Medical and Biological Engineering 2010.	en_US
dc.relation.ispartof	Medical and Biological Engineering and Computing	en_US
dc.relation.isbasedon	10.1007/s11517-010-0579-0	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Leg	en_US
dc.subject.mesh	Brain	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Imagination	en_US
dc.subject.mesh	Discrimination (Psychology)	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	User-Computer Interface	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Young Adult	en_US
dc.subject.mesh	Discrimination, Psychological	en_US
dc.title	Discrimination of left and right leg motor imagery for brain-computer interfaces	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	48	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Antalya, Turkey
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 Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The results demonstrate that MMI of the left and right leg produce a contralateral preponderance of EEG alpha band desynchronization, which can be spatially discriminated. This suggests that lower extremity MMI could provide signals for additional BCI channels. The study also shows that movement imitation enhances alpha band desynchronization during MMI, and might provide a useful aid in the identification and training of BCI signals. © International Federation for Medical and Biological Engineering 2010.

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

http://hdl.handle.net/10453/13521