Decreasing road accidents rate and increasing
road safety have been the major concerns for a long time as
traffic accidents expose the divers, passengers, properties to
danger. Driver fatigue and drowsiness are one of the most
critical factors affecting road safety, especially on highways.
EEG signal is one of the reliable physiological signals used to
perceive driver fatigue state but wearing a multi-channel
headset to acquire the EEG signal limits the EEG based systems
among drivers. The current work suggested using a driver
fatigue detection system using transfer learning, depending only
on one EEG channel to increase system usability. The system
firstly acquires the signal and passing it through preprocessing
filtering then, converts it to a 2D spectrogram. Finally, the 2D
spectrogram is classified with AlexNet using transfer learning to
classify it either normal or fatigue state. The current study
compares the accuracy of seven EEG channel to select one of
them as the most accurate channel to depend on it for
classification. The results show that the channels FP1 and T3 are
the most effective channels to indicate the drive fatigue state.
They achieved an accuracy of 90% and 91% respectively.
Therefore, using only one of these channels with the modified
AlexNet CNN model can result in an efficient driver fatigue
detection system. |
