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dc.contributor.authorMousa Kadhim, Wali
dc.contributor.authorMurugappan, Muthusamy, Dr.
dc.contributor.authorR. Badlishah, Ahmad, Prof. Dr.
dc.date.accessioned2014-06-18T05:02:37Z
dc.date.available2014-06-18T05:02:37Z
dc.date.issued2013-06
dc.identifier.citationJournal of Theoretical and Applied Information Technology, vol. 52(3), 2013, pages 268-272en_US
dc.identifier.issn1992-8645 (P)
dc.identifier.issn1817-3195 (O)
dc.identifier.urihttp://www.jatit.org/volumes/Vol52No3/fiftysecond_3_2013.php
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/35669
dc.descriptionLink to publisher's homepage at http://www.jatit.org/en_US
dc.description.abstractIn this work, we classify the driver drowsiness level (awake, drowsy, high drowsy and sleep stage1) based on different wavelets and probabilistic neural network classifier using wireless EEG signals. Deriving the amplitude spectrum of four different frequency bands delta, theta, alpha, and beta of EEG signals. Comparing the results of PNN based on spectral centroid, and power spectral density features extracted by different wavelets (db4, db8, sym8, and coif5) from the amplitude spectrum of the said bands. As results of this study indicates that the best average accuracy achieved of 61.16% based on power spectral density feature extracted by db4 wavelet.en_US
dc.language.isoenen_US
dc.publisherJATIT & LLS. All rights reserveden_US
dc.subjectDiscrete wavelet transformen_US
dc.subjectEEGen_US
dc.subjectFast fourier transformen_US
dc.subjectProbabilistic neural networken_US
dc.titlePNN based driver drowsiness level classification using EEGen_US
dc.typeArticleen_US
dc.contributor.urlmusawali@yahoo.comen_US
dc.contributor.urlmurugappan@unimap.edu.myen_US
dc.contributor.urlbadli@unimap.edu.myen_US


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