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Article

Temporal EEG Imaging for Drowsy Driving Prediction

1
Department of Electrical Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
2
Centre for Artificial Intelligence, University of Technology Sydney, Sydney 2007, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5078; https://doi.org/10.3390/app9235078
Received: 21 October 2019 / Revised: 20 November 2019 / Accepted: 21 November 2019 / Published: 25 November 2019
(This article belongs to the Special Issue Human Health Engineering Volume II)
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. View Full-Text
Keywords: electroencephalography; deep learning; driving fatigue; feature extraction; convolutional neural network electroencephalography; deep learning; driving fatigue; feature extraction; convolutional neural network
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MDPI and ACS Style

Cheng, E.J.; Young, K.-Y.; Lin, C.-T. Temporal EEG Imaging for Drowsy Driving Prediction. Appl. Sci. 2019, 9, 5078. https://doi.org/10.3390/app9235078

AMA Style

Cheng EJ, Young K-Y, Lin C-T. Temporal EEG Imaging for Drowsy Driving Prediction. Applied Sciences. 2019; 9(23):5078. https://doi.org/10.3390/app9235078

Chicago/Turabian Style

Cheng, Eric J., Ku-Young Young, and Chin-Teng Lin. 2019. "Temporal EEG Imaging for Drowsy Driving Prediction" Applied Sciences 9, no. 23: 5078. https://doi.org/10.3390/app9235078

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