A novel real-time driving fatigue detection system based on wireless dry EEG

Wang, Hongtao; Dragomir, Andrei; Abbasi, Nida Itrat; Li, Junhua; Thakor, Nitish V.; Bezerianos, Anastasios

doi:10.1007/s11571-018-9481-5

Cited by 101 publications

(58 citation statements)

References 35 publications

(32 reference statements)

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“…In this study, we only employed spectral power features for fatigue estimation. Other features, such as functional connectivity [40,41] and entropy [20,42], could be used in the proposed framework as these features have been proven to be of discriminative power in the differentiation between alertness and fatigue. Most recently, high-order functional connectivity in both static and dynamic representations was found to have complementary information to low-order functional connectivity in fatigue detection [43].…”

Section: Ieee Transactions On Cognitive and Developmental Systemsmentioning

confidence: 99%

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Bose

Wang

Dragomir

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

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Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

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Section: Ieee Transactions On Cognitive and Developmental Systemsmentioning

confidence: 99%

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Bose

Wang

Dragomir

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

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“…Previous studies have utilized spectral powers as indicators of driving drowsiness and mental fatigue [2], [12]- [15]. Spectral powers in typical frequency bands (i.e., theta, alpha, and beta) have been found to be closely related to driving drowsiness.…”

Section: Introductionmentioning

confidence: 99%

Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment

Harvy

Thakor

Bezerianos

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Previous studies exploring driving drowsiness utilized spectral power and functional connectivity without considering between-frequency and more complex synchronizations. To complement such lacks, we explored interregional synchronizations based on the topographical and dynamic properties between frequency bands using high-order functional connectivity (HOFC) and envelope correlation. We proposed the dynamic interactions of HOFC, associated-HOFC, and a global metric measuring the aggregated effect of the functional connectivity. The EEG dataset was collected from 30 healthy subjects, undergoing two driving sessions. The two-session setting was employed for evaluating the metric reliability across sessions. Based on the results, we observed reliably significant metric changes, mainly involving the alpha band. In HOFC θα , HOFC αβ , associated-HOFC θα , and associated-HOFC αβ , the connection-level metrics in frontal-central, central-central, and central-parietal/occipitalareas were significantly increased, indicating a dominance in the central region. Similar results were also obtained in the HOFC θαβ and aHOFC θαβ. For dynamic-low-order-FC and dynamic-HOFC, the global metrics revealed a reliably significant increment in the alpha, theta-alpha, and alpha-beta bands. Modularity indexes of associated-HOFC α and associated-HOFC θα also exhibited reliably significant differences. This paper demonstrated that within-band and betweenfrequency topographical and dynamic FC can provide complementary information to the traditional individual-band LOFC for assessing driving drowsiness.

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“…In order to build a predictive model, the authors used the elastic net regularized multinomial logistic regression and applied it on data obtained from the Second Strategic Highway Research Program (SHRP 2). The work in [19] presented a real-time driving fatigue detection methodology based on Electroencephalographic (EEG) signals; the objective of such a system was to detect mental fatigue, thus preventing possible crashes. In [20], a crash prediction system is presented: the proposed system's decisions were based on the driver's behavioral and physiological features.…”

Section: Driver Fatigue Based Approachesmentioning

confidence: 99%

Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

et al. 2018

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With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

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A novel real-time driving fatigue detection system based on wireless dry EEG

Cited by 101 publications

References 35 publications

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment

Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

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