Enhancement of Morlet Mother Wavelet in Time–Frequency Domain in Electroencephalogram (EEG) Signals for Driver Fatigue Classification

Abstract

Driving is hazardous due to various factors, including driving attitudes, road type, and driving perceptual environment. These influences factors may cause a fatigue condition. Moreover, less driving experience and lack of alertness can also be contributed to dangerous accidents. Fatigued driving is a key factor in car accidents worldwide because of sleep disorders and driving durations. An EEG signal is used to determine changes in brain activity for diagnosing driver fatigue states. Artifacts were removed using independent component analysis (ICA) in the preprocessing stage. Then, features are extracted from the temporal region of the brain using eight channels (Fp1, Fp2, O1, O2, F4, F3, P4, and P3). The frequency bands used are alpha, delta, and theta. In continuous wavelet transform analysis, the Morlet wavelet is a fast wavelet transform in time–frequency analysis. Still, it has shift sensitivity and lacks phase information, affecting the frequency resolution analysis. This study proposes the enhancement of the Morlet mother wavelet for frequency resolution in the time–frequency domain using independent component analysis to overcome the drawbacks of the Morlet wavelet. The proposed technique can increase the percentage of driver fatigue classification accuracy of EEG signals. Then, the artificial neural network (ANN) classifier with Levenberg–Marquardt (LM) training algorithm gives the highest accuracy of the classification results with 97.40%, followed by the k-nearest neighbor (KNN) with 95.83% and the support vector machine (SVM) with 83%.