Driver Fatigue Detection with Single EEG Channel Using Transfer Learning

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.