Two channel EEG thought pattern classifier

Craig, DA; Nguyen, HT; Burchey, HA

Two channel EEG thought pattern classifier

Craig, DA Nguyen, HT

Burchey, HA

Permalink

Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2006, pp. 1291 - 1294
Issue Date:: 2006-12-01

Closed Access

	Filename	Description	Size
	2006005643.pdf		159.13 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.contributor.author	Burchey, HA	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2006, pp. 1291 - 1294	en_US
dc.identifier.isbn	1424400325	en_US
dc.identifier.isbn	9781424400324	en_US
dc.identifier.issn	0589-1019	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3174
dc.description.abstract	This paper presents a real-time Electro-Encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O1 and C4 the system can classify three mental commands (forward, left and right) with an accuracy of more than 79%. © 2006 IEEE.	en_US
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en_US
dc.relation.isbasedon	10.1109/IEMBS.2006.260157	en_US
dc.subject.mesh	Brain	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Imagination	en_US
dc.subject.mesh	Thinking	en_US
dc.subject.mesh	Task Performance and Analysis	en_US
dc.subject.mesh	Evoked Potentials	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	User-Computer Interface	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Two channel EEG thought pattern classifier	en_US
dc.type	Conference Proceeding
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	New York, USA	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 Electro-Encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O1 and C4 the system can classify three mental commands (forward, left and right) with an accuracy of more than 79%. © 2006 IEEE.

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

http://hdl.handle.net/10453/3174