Abstract
As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver’s EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.
Highlights
The prevention of drowsy driving has become a major challenge in safety driving issues
The evaluation results show that the proposed method can improve the performance of EEG image-based brain-computer interfaces (BCIs) systems in drowsiness prediction
It is challenging to classify EEG data without an artifact removal process because drivers’ brain activity can change over time due to many factors, such as their mental state and body movement, which result in the temporal fluctuations of the EEG signals
Summary
The prevention of drowsy driving has become a major challenge in safety driving issues. ICA is a powerful tool for extracting brain activity from raw EEGsignal, it cannot support real-time applications because the separated artifacts need to be removed manually. This study does not apply any artifact removal process to the raw EEG data during the experiment, ensuring that the proposed method does not use manual processes for the drowsy driving prediction task. The driving performance may not immediately decrease with increasing drowsiness levels, which means that drivers maintain normal driving performance even though their vigilance level has started to decrease To overcome these difficulties, this study proposes a new EEG image method that combines multiple frames of EEG images to examine the temporal activity of EEG data. The evaluation results show that the proposed method can improve the performance of EEG image-based BCI systems in drowsiness prediction
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