Early driver fatigue detection from electroncephalography signals using artificial neural networks

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dc.contributor.author King, Leslie en_US
dc.contributor.author Nguyen, Hung en_US
dc.contributor.author Lal, Sara en_US
dc.contributor.editor N/A en_US
dc.date.accessioned 2009-11-09T05:39:16Z
dc.date.available 2009-11-09T05:39:16Z
dc.date.issued 2006 en_US
dc.identifier 2006005648 en_US
dc.identifier.citation King Leslie, Nguyen Hung, and Lal Saroj 2006, 'Early driver fatigue detection from electroncephalography signals using artificial neural networks', IEEE, New York, USA, pp. 2187-2190. en_US
dc.identifier.issn 14244-0033-3 en_US
dc.identifier.other E1 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.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/IEMBS.2006.259231 en_US
dc.title Early driver fatigue detection from electroncephalography signals using artificial neural networks en_US
dc.parent Proceedings of the 28th IEEE EMBS Annual International Conference en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.identifier.startpage 2187 en_US
dc.identifier.endpage 2190 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference.location New York, USA en_US
dc.for 090305 en_US
dc.personcode 044342 en_US
dc.personcode 840115 en_US
dc.personcode 010322 en_US
dc.percentage 100 en_US
dc.classification.name Rehabilitation Engineering en_US
dc.classification.type FOR-08 en_US
dc.custom Annual International Conference of the IEEE Engineering in Medicine and Biology Society en_US
dc.date.activity 20060830 en_US
dc.location.activity New York, USA en_US
dc.description.keywords Driver fatigue, EEG, Neural Network en_US
dc.staffid 010322 en_US


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