EEG-based driver fatigue detection using hybrid deep generic model

San, Phyo Phyo; Ling, Sai Ho; Chai, Rifai; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.

doi:10.1109/embc.2016.7590822

Cited by 27 publications

(9 citation statements)

References 16 publications

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“…But the classification running time was not reported in this study. In [38], Deep Generic Model (DGM)-SVM was utilized to classify EEG data. Their best classification accuracy was 73.29%.…”

Section: Final Classificationmentioning

confidence: 99%

Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

Wang¹,

Johnson²,

Lin³

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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To investigate the correlation between electroencephalogram (EEG) and driving fatigue states, this study used machine learning algorithms to detect driving fatigue based on EEG. EEG data of 14 channels were collected from thirty-four healthy subjects in this research at Northeastern University. Each subject participated in two scenarios (baseline and fatigue scenarios). Subjective ratings of fatigue levels were also obtained from the subjects using the NASA-Task Load Index (TLX). The Common Spatial Pattern (CSP) algorithm was used to extract features from the raw EEG data. The Support Vector Machine (SVM) was used as the classifier in the design of the machine learning algorithm. A grid search cross validation was exploited to find optimal hyperparameter settings. The best classification result was 90%, using all 14 EEG channels and linear kernel of SVM. The experimental results proved that a machine learning algorithm was reliable to classify driving fatigue states using EEG data. This study demonstrated that CSP and SVM were promising in detecting driving fatigue, and can, therefore, be strong foundations in future efforts to reduce traffic accidents and save thousands of human lives.

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“…But the classification running time was not reported in this study. In [38], Deep Generic Model (DGM)-SVM was utilized to classify EEG data. Their best classification accuracy was 73.29%.…”

Section: Final Classificationmentioning

confidence: 99%

Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

Wang¹,

Johnson²,

Lin³

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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“…The deep belief network (DBN) based DDD system obtained 91.10 % of sensitivity and 55.48 % of specificity. The same approach was also utilized in [29] by using the CNN model on EEG signals. The authors reported that deep-learning methods achieved better accuracy compare to traditional machinelearning algorithms [21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Abbas¹

2020

IJACSA

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Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save roadside accidents. To help reduce the mortality rate, the "HybridFatigue" DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heartbeat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.

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“…San et al [35] used a deep generic model (DGM) and support vector machine to detect driver fatigue. They compared the performance of power spectrum density features-based SVM and deep generic modelbased SVM and found that DGM-based SVM performed better in terms of sensitivity, specifity and accuracy.…”

Section: Dong and Wumentioning

confidence: 99%

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Naz¹,

Ziauddin²,

Raza³

2019

IJIMAI

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Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes.

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EEG-based driver fatigue detection using hybrid deep generic model

Cited by 27 publications

References 16 publications

Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

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