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dc.contributor.authorMousa Kadhim, Wali
dc.contributor.authorMurugappan, Muthusamy, Dr.
dc.contributor.authorR. Badlishah, Ahmad, Prof. Dr.
dc.date.accessioned2014-06-18T04:25:56Z
dc.date.available2014-06-18T04:25:56Z
dc.date.issued2013
dc.identifier.citationPrzeglad Elektrotechniczny, vol. 89(6), 2013, pages 113-117en_US
dc.identifier.issn0033-2097
dc.identifier.urihttp://pe.org.pl/issue.php?lang=0&num=06/2013
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/35668
dc.descriptionLink to publisher's homepage at http://pe.org.pl/en_US
dc.description.abstractIn this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical features (spectral centroid, and power spectral density) were extracted from four EEG frequency bands (delta, theta, alpha, and beta) using Fast Fourier Transform (FFT). These features are used to classify the driver drowsiness level using three classifiers namely, subtractive fuzzy clustering, probabilistic neural network, and K nearest neighbour. Results of this study indicates that the best average accuracy of 84.41% is achieved using subtractive fuzzy classifier based on power spectral density feature extracted by db4 wavelet function.en_US
dc.language.isoenen_US
dc.publisherPrzegląd Elektrotechnicznyen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectEEGen_US
dc.subjectFast Fourier Transformen_US
dc.subjectFuzzy inference systemen_US
dc.titleClassification of driver drowsiness level using wireless EEGen_US
dc.title.alternativeBadania senności kierowcy na podstawie sygnału 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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