Controlling a human-computer interface system with a novel classification method that uses electrooculography signals

Wu, SL; Liao, LD; Lu, SW; Jiang, WL; Chen, SA; Lin, CT

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals

Wu, SL Liao, LD Lu, SW Jiang, WL Chen, SA Lin, CT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Biomedical Engineering, 2013, 60 (8), pp. 2133 - 2141
Issue Date:: 2013-08-05

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Field	Value	Language
dc.contributor.author	Wu, SL	en_US
dc.contributor.author	Liao, LD	en_US
dc.contributor.author	Lu, SW	en_US
dc.contributor.author	Jiang, WL	en_US
dc.contributor.author	Chen, SA	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-08-05	en_US
dc.identifier.citation	IEEE Transactions on Biomedical Engineering, 2013, 60 (8), pp. 2133 - 2141	en_US
dc.identifier.issn	0018-9294	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116959
dc.description.abstract	Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future. © 1964-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Biomedical Engineering	en_US
dc.relation.isbasedon	10.1109/TBME.2013.2248154	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Eye Movements	en_US
dc.subject.mesh	Electrooculography	en_US
dc.subject.mesh	Telemetry	en_US
dc.subject.mesh	Equipment Design	en_US
dc.subject.mesh	Equipment Failure Analysis	en_US
dc.subject.mesh	Electrodes	en_US
dc.subject.mesh	Man-Machine Systems	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Brain-Computer Interfaces	en_US
dc.title	Controlling a human-computer interface system with a novel classification method that uses electrooculography signals	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	60	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	60	en_US

Abstract:

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future. © 1964-2012 IEEE.

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

http://hdl.handle.net/10453/116959