Distraction detection of driver based on EEG signals in a simulated driving with alternative secondary task

Driving distraction is a main human factor of traffic accidents. Distraction would seriously affect the drivers’ cognitive process, inducing the inability to fully perceive the surrounding environment, make the correct judgments and perform the proper operations in time. It is important to identify the drivers’ attentional state accurately and quickly during the driving process. The objective of this study was to develop a novel driving distraction detection method based on electroencephalographic (EEG) signals. A simultaneous driving and distraction experiment was designed, in which the alternative secondary tasks with a 2-back paradigm were utilized to induce the visual or auditory distraction. The EEG signals of 22 subjects were analysed to distinguish the focused state of the driver from distraction. Results indicated that the proposed method based on EEGNet and long short-term memory (LSTM) provided an average classification accuracy of 71.1% in three-class classification. Reducing the number of the electrodes from 63 to 14 would not significantly reduce the accuracy so that a higher model efficiency could be obtained.