Driving Attention State Detection Based on GRU-EEGNet

Abstract

The present study utilizes the significant differences in θ, α, and β band power spectra observed in electroencephalograms (EEGs) during distracted versus focused driving. Three subtasks, visual distraction, auditory distraction, and cognitive distraction, were designed to appear randomly during driving simulations. The θ, α, and β band power spectra of the EEG signals of the four driving attention states were extracted, and SVM, EEGNet, and GRU-EEGNet models were employed for the detection of the driving attention states, respectively. Online experiments were conducted. The extraction of the θ, α, and β band power spectrum features of the EEG signals was found to be a more effective method than the extraction of the power spectrum features of the whole EEG signals for the detection of driving attention states. The driving attention state detection accuracy of the proposed GRU-EEGNet model is improved by 6.3% and 12.8% over the EEGNet model and PSD_SVM method, respectively. The EEG decoding method combining EEG features and an improved deep learning algorithm, which effectively improves the driving attention state detection accuracy, was manually and preliminarily selected based on the results of existing studies.