Early driver fatigue detection from electroencephalography signals using artificial neural networks.

King, LM; Nguyen, HT; Lal, SK

Early driver fatigue detection from electroencephalography signals using artificial neural networks.

King, LM Nguyen, HT

Lal, SK

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Publication Type:: Conference Proceeding
Citation:: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2006, pp. 2187 - 2190
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	King, LM	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Lal, SK https://orcid.org/0000-0002-0911-0850	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2006, pp. 2187 - 2190	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3178
dc.description.abstract	This paper describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver fatigue. The neural network uses a training optimization technique called the magnified gradient function (MGF). This technique reduces the time required for training by modifying the standard back propagation (SBP) algorithm. The MGF is shown to classify professional driver fatigue with 81.49% accuracy (80.53% sensitivity, 82.44% specificity) and non-professional driver fatigue with 83.06% accuracy (84.04% sensitivity and 82.08% specificity).	en_US
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Mental Fatigue	en_US
dc.subject.mesh	Task Performance and Analysis	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Automobile Driving	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Early driver fatigue detection from electroencephalography signals using artificial neural networks.	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/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
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 describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver fatigue. The neural network uses a training optimization technique called the magnified gradient function (MGF). This technique reduces the time required for training by modifying the standard back propagation (SBP) algorithm. The MGF is shown to classify professional driver fatigue with 81.49% accuracy (80.53% sensitivity, 82.44% specificity) and non-professional driver fatigue with 83.06% accuracy (84.04% sensitivity and 82.08% specificity).

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

http://hdl.handle.net/10453/3178