Drowsy Driving Detection by EEG Analysis Using Wavelet Transform and K-means Clustering

Abstract This research aims to develop a driver drowsiness monitoring system by analyzing the electroencephalographic (EEG) signals in a software scripted environment and using a driving simulator. These signals are captured by a multi-channel electrode system. Any muscle movement impacts the EEG signal recording which translates to artifacts. Therefore, noise from the recording is eliminated by subtracting the noisy signal from the original EEG recording. The actual EEG signals are then subjected to band pass filtering with cut-off frequencies 0.5 Hz and 100 Hz. The filtered signals are analyzed using a time-frequency technique known as the Discrete Wavelet Transform (DWT). A third order Debauchies’ wavelet and five level decomposition is utilized to segregate the signal into five sub-bands, namely, delta (0.5 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 12 Hz), beta (12 – 30 Hz) and gamma (> 30 Hz). First order statistical moments such as mean, median, variance, standard deviation and mode of the sub-bands are calculated and stored as features. These features serve as an input to the next stage of system classification. Unsupervised learning through K-means clustering is employed since the classes of the signals are unknown. This provides a strong decision making tool for a real-time drowsiness detection system. The algorithm developed in this work has been tested on twelve samples from the Physionet sleep-EDF database.