Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained From Scalp EEG Signals

Chaudhuri, Aritra; Routray, Aurobinda

doi:10.1109/tits.2018.2890332

Cited by 49 publications

(23 citation statements)

References 62 publications

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“…Their ground truth labels were based on Li’s subjective fatigue scale and the accuracy achieved was 88.74%. Chaudhuri and Routray [ 177 ] used only three entropies as features—ApEn, SampEn, and modified SampEn. Their experiment was designed to slowly increase the fatigue level of the participants because of the effects of physical and mental workload, along with the effects of sleep deprivation.…”

Section: Driver Drowsiness Detection Systemsmentioning

confidence: 99%

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Stančin¹,

Cifrek²,

Jović³

2021

Sensors

128

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Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

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Section: Driver Drowsiness Detection Systemsmentioning

confidence: 99%

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Stančin¹,

Cifrek²,

Jović³

2021

Sensors

128

View full text Add to dashboard Cite

show abstract

“…Other studies were conducted on the health of drivers [ 14 , 23 , 24 , 25 , 26 ], in order to analyze when the driver is no longer able to drive and detect the first signs of fatigue for anticipating the onset of drowsiness to alert the driver that it is necessary to take a break. For this type of method, it uses Electroencephalography (EEG), which makes measurements using the captures pasted directly on the skin of the driver by measuring heart rate, body temperature, and other measurements depending on the type of work.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the environment surrounding the driver is a factor that influences the driver’s degree of concentration and acts on his nervousness [ 10 ]. In addition, many methods related to the study of driver fatigue have been proposed in the literature [ 11 , 12 , 13 , 14 ]. However, these methods analyze the driver’s state via sensors installed either on the car or the driver directly.…”

Section: Introductionmentioning

confidence: 99%

Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

2020

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In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver’s behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver’s face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents.

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“…About 1.3 million people lose their lives in traffic accidents every year in the world [2], and fatigued driving is a leading factor in it [3]. Thus, performing mental state detection and prediction while driving is extremely important to reduce losses of lives and property caused by fatigued driving [4,5].…”

Section: Introductionmentioning

confidence: 99%

An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction

Zeng

Borghini

et al. 2021

Sensors

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Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain–computer interaction (BCI).

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Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained From Scalp EEG Signals

Cited by 49 publications

References 62 publications

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction

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