The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

Peng, Yong; Xu, Qian; Lin, Shuxiang; Wang, Xinghua; Xiang, Guoliang; Huang, Shufang; Zhang, Honghao; Fan, Chaojie

doi:10.3389/fpsyg.2022.919695

Cited by 11 publications

(5 citation statements)

References 139 publications

(121 reference statements)

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“…The study's primary goals are to accurately detect fatigue, develop standardized measures, and evaluate systems under realistic driving conditions [34]. Study [35] proposes a wide range of solutions, such as the creation of comprehensive datasets, the improvement of data aggregation methods, the development of real-time detection capabilities, and the design of low-cost detection environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Driver Drowsy and Yawn System Alert Using Deep Cascade Convolution Neural Network DCCNN

ABBOOD,

Yonan

2023

ijcsm

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Driver fatigue and lethargy are widely acknowledged as significant contributors to the occurrence of vehicular accidents. During the preceding decade, there were a total of 66,000 motor vehicle accidents, resulting in 22,952 fatalities and 79,545 injuries. It is of the utmost importance to implement measures designed to notify drivers in order to prevent accidents and save lives. The purpose of this research was to develop and implement a Driving Drowsiness Detection System (DDDS) in order to increase road safety. The system employs eye and mouth movements as indicators in order to detect fatigue and reduce the likelihood of accidents. Using a high-resolution camera and Deep Cascaded Convolutional Neural Networks (DCCNN) to accomplish accurate drowsiness detection was the objective of this research. This study's methodology entails the analysis of driver behavior, with a particular emphasis on eye movements captured by a high-resolution camera. The facial region was identified using landmarks extracted from the Dlib toolset, and the "Eyes Aspect Ratio" metric was developed to quantify the level of fatigue. Deep Convolutional Neural Network (DCCNN) output triggers an alarm on the dashboard of the graphical user interface (GUI). The study utilized an image with 450x320 pixels and a frame rate of 60 frames per second. In ideal lighting conditions, drowsy detection accuracy exceeded 99.9%, while in less than ideal lighting conditions, it surpassed 99.8%. Compared to previous research, the present study demonstrated a higher level of accuracy in detecting drowsiness.

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Section: Literature Reviewmentioning

confidence: 99%

Driver Drowsy and Yawn System Alert Using Deep Cascade Convolution Neural Network DCCNN

ABBOOD,

Yonan

2023

ijcsm

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“…Thus, the 2 most commonly studied types of distractions during driving are visual distraction, also known as “eye-off-road” distraction, and cognitive distraction, referred to as “mind-off-road” distraction [ 3 ]. Visual distraction primarily involves external stimuli such as looking at mobile phone, and cognitive distraction engages an internal process when the driver’s mind is occupied with nondriving-related thoughts [ 4 ]. Both of these types of distractions can greatly undermine driver’s attention and performance [ 5 , 6 ], albeit through different mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

Qi,

Liu,

Guan

et al. 2024

Cyborg Bionic Syst

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In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain–computer interface in autonomous driving.

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“…To understand cognition from a neural level, we collected EEG measures of driver workload and engagement, consistent with McDonnell et al (2021b). EEG allows for direct measurement of brain activity in response to the varying demands of the driving environment (Lohani et al, 2019;Peng et al, 2022). The EEG signal can be decomposed into the frequency domain using a Fourier analysis (Cohen, 2014) and then categorized into different frequency bands (e.g., theta ∼ 4-8 Hz, alpha ∼8-12 Hz, beta ∼12-30 Hz) that index different cognitive processes.…”

Section: Introductionmentioning

confidence: 99%

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving

McDonnell,

Crabtree,

Cooper

et al. 2023

Hum Factors

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Objective This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. Background Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect “edge cases” that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. Method Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions—with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. Results DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload—though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. Conclusion Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. Application These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

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The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

Cited by 11 publications

References 139 publications

Driver Drowsy and Yawn System Alert Using Deep Cascade Convolution Neural Network DCCNN

Driver Drowsy and Yawn System Alert Using Deep Cascade Convolution Neural Network DCCNN

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving

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