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

<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods behavioral methods. The most objective method for detection is the method. One of used an electroencephalogram (EEG). In this research wavelet transform a feature extraction using support vector machine (SVM) classifier. We proposed experiment retrieval data which designed by modified-EAR EEG signal. From SVM training process, with 5-fold cross validation, Quadratic kernel has highest accuracy 84.5% then others. testing Driving-2 process 7 respondents were detected class, 3 awake class. Driving-3 6 4 </span></span>