Strengthening association between driver drowsiness and its physiological predictors by combining EEG with measures of body movement

Pritchett, Stacey; Zilberg, Eugene; Xu, Zheng; Karrar, Murad; Lal, Sara; Burton, David L.

doi:10.1109/ib2com.2011.6217901

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…Based on the results as shown in Fig. 3, the common channels that were shared across the majority (>95%) of the subjects were located in the frontal, parietal and occipital regions, which is consistent with the relevant regions for mental fatigue estimation identified in the previous papers (i.e., frontal region reported in [1,12,19], parietal region reported in [10,19] and occipital region reported in [1,11,13,14,23,38]). All aforementioned brain regions have also been detected by recent work [18].…”

Section: Discussionsupporting

confidence: 87%

“…Past studies showed different frequency bands relevant to fatigue. For instance, EEG spectra in alpha and theta bands [11]; delta, theta, alpha and beta bands derived from a single channel electrode [12]; delta and theta bands [13]; alpha burst features were used in [14] and shown to be sensitive to mental fatigue. Theta band was also shown to be indicative of the effects of fatigue [15,16].…”

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confidence: 99%

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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: Discussionsupporting

confidence: 87%

mentioning

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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“…One of them recorded contextual information using motion sensor; while the remaining two studies recorded contextual information using physiological signals. Specifically, Pritchett et al [ 47 ] also proposed an EEG-based context-aware solution for DDD. The main difference between the current system and that presented in [ 47 ] is that they recorded contextual information from the driver’s seat where a piezoelectric film sensor is attached rather than from the headset directly.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, Pritchett et al [ 47 ] also proposed an EEG-based context-aware solution for DDD. The main difference between the current system and that presented in [ 47 ] is that they recorded contextual information from the driver’s seat where a piezoelectric film sensor is attached rather than from the headset directly. In addition, unlike the SVM model used in our study, they used linear regression model to estimate the drowsiness level (dependent variable), in which a wide range of α burst parameters and body movement parameters were used as features (independent variables).…”

Section: Discussionmentioning

confidence: 99%

A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Chung

2015

Sensors

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Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.

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“…All the spindle features outperformed the pure α power, thus establishing the spindle rate as the best feature. Pritchett et al explored α rhythm by analyzing its burst duration, mean amplitude, relative amplitude, amplitude variance, wave duration variance, wave similarity, and slope smoothness [50]. Kalauzi et al proposed a method for analyzing the phase information of α rhythm [63].…”

Section: Fft+mentioning

confidence: 99%

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Chung

2022

Sensors

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Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.

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Strengthening association between driver drowsiness and its physiological predictors by combining EEG with measures of body movement

Cited by 6 publications

References 23 publications

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

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