Driver fatigue and losing wariness during long driving hours is considered as one of the main road
accidents causes. It affects road safety directly. Road safety is a major disquieting problem, since traffic
accidents endanger divers, travelers, and everyone in their scope, in addition to the road and vehicle
damages. The EEG signal becomes one of the most dependable biological signals utilized to estimate the
drivers' drowsiness state, although a multichannel acquiring system must be used to transmit the EEG
signal. Wearing a multi-channel headset is not readily accepted by drivers. Many attempts have been done
by researchers to reduce number of EEG channels used to detect drivers’ fatigue. The present study
proposed utilizing only one of EEG channels signal to estimate driver fatigue state to raise the acceptance
of the system and its flexibility. The system starts with receiving the EEG signals, then pre-processing
them using filtering and transformed them to color image using spectrogram. After that, the EEGs
spectrogram passed to the proposed CNN deep network model to identify them either fatigue or normal
fatigue. The present study measured up many EEG channels to identify the most accurate and dependable
one to classify driver fatigue. The results indicate that the FP1, T3, and Oz channels considered as the
most efficient channels to identify the drive’s state either fatigue or not. They achieved an accuracy of
94.33%, 92.57 and 93% respectively. Therefore, using a single one of these channels and the proposed
CNN model will lead to a more robust driver drowsiness/fatigue detection system using EEG signals. |
