Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

Sobhan Sheykhivand,Saeed Meshgini,Ali Farzamnia,Somaye Makoui,Tohid Yousefi Rezaii

doi:10.3390/su14052941

Sobhan Sheykhivand, Saeed Meshgini + Show 3 more

Open Access

https://doi.org/10.3390/su14052941

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Abstract

In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue.

Highlights

Academic Editors: KumaranWith the advancement of industrial technology in recent years, car production has increased dramatically, resulting in an increase in traffic accidents
In our proposed algorithm, the compressed signal is used as the deep convolutional long short-term memory (DCLSTM) network input, for which Table 3 shows the accuracy obtained on the validation data
The networks to be compared included MLP [60], DBM [61], support vector machine (SVM) [62], and Fully CNN (FCNN) [63], and they are based on feature learning from raw data, as well as manual feature extraction, which have recently been widely used in driver-fatigue detection studies

Summary

Introduction

With the advancement of industrial technology in recent years, car production has increased dramatically, resulting in an increase in traffic accidents. 1.25 million people die in road accidents, according to the World Health Organization (WHO) [1]. Driver fatigue can be considered the main cause of road fatalities among the factors affecting car accidents. According to the National Highway Traffic Safety Administration (NHTSA), 100,000 driver fatigue accidents caused 1550 deaths, 71,000 injuries, and USD 12.5 billion in monetary losses, annually, in the United States [2]. A brief mathematical introduction to CS theory and DNNs for the automatic detection of driver fatigue is described. We provide a brief description of CS theory [39]. In view of the above, the CS theory can be applied to nonsparse signals, provided that the sparsity of the transformed signal is guaranteed.

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