Improving classification rates for use in fatigue countermeasure devices using brain activity

Tran, Y; Craig, A; Wijesuriya, N; Nguyen, H

Improving classification rates for use in fatigue countermeasure devices using brain activity

Tran, Y

Craig, A

Wijesuriya, N Nguyen, H

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Publication Type:: Conference Proceeding
Citation:: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 2010, pp. 4460 - 4463
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Tran, Y https://orcid.org/0000-0002-1741-4205	en_US
dc.contributor.author	Craig, A https://orcid.org/0000-0001-7647-7604	en_US
dc.contributor.author	Wijesuriya, N	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 2010, pp. 4460 - 4463	en_US
dc.identifier.isbn	9781424441235	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16355
dc.description.abstract	Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective selfreport of fatigue with classification accuracy improving to 84.5%. © 2010 IEEE.	en_US
dc.relation.ispartof	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10	en_US
dc.relation.isbasedon	10.1109/IEMBS.2010.5625964	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Fatigue	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Brain Mapping	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Task Performance and Analysis	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Automobile Driving	en_US
dc.subject.mesh	Technology Assessment, Biomedical	en_US
dc.title	Improving classification rates for use in fatigue countermeasure devices using brain activity	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Buenos Aires, Argentina	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 Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective selfreport of fatigue with classification accuracy improving to 84.5%. © 2010 IEEE.

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

http://hdl.handle.net/10453/16355