EEG signal classification for drowsiness detection using wavelet transform and support vector machine

Novie Theresia Br. Pasaribu, Timotius Halim, Ratnadewi Ratnadewi, Agus Prijono

Abstract


There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods, and behavioral methods, etc. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using Support Vector Machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. 

Keywords


Drowsiness; Electroencephalogram; Support vector machine; Wavelet transform



DOI: http://doi.org/10.11591/ijai.v10.i3.pp%25p

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