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

Novie Theresia Br. Pasaribu (Universitas Kristen Maranatha)
Timotius Halim (Universitas Kristen Maranatha)
Ratnadewi Ratnadewi (Universitas Kristen Maranatha)
Agus Prijono (Universitas Kristen Maranatha)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 2: June 2021

There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. 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.