A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jing-Ping; Wang, Chunbai

doi:10.3390/s16020242

Cited by 62 publications

(29 citation statements)

References 39 publications

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“…In most of the ERP studies, consistent finding is that the magnitude of P300 components decreased with vigilance decrement. Correspondingly, recent studies have reported a correlation between EEG rhythmic components and vigilance levels [33][34][35][36]. For example, studies have shown that activities in the alpha rhythm increased over the frontal and parietal-occipital areas with increasing TOT [37][38][39].…”

Section: Introductionmentioning

confidence: 89%

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States

et al. 2019

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In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05.

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Section: Introductionmentioning

confidence: 89%

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States

et al. 2019

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“…However, the train drivers, signallers, and controllers are also important to railway safety. Especially in case of an emergency, the driver’s performance is a major contributor to incidents and accidents [2,3,4]. It is crucial to analyse the contribution of the driver’s performance to railway safety and accidents.…”

Section: Introductionmentioning

confidence: 99%

“…Proven techniques of signals processing, such as date compressing, de-noising, feature extraction and classification, make it be possible to cope with the EEG single. Principal Component Analysis (PCA), independent component analysis (ICA), sparse representation and compressed sensing and so on are widely applied to EEG detection [2,34,35,36,37,38]. The literature shows that a typical sleep stage can be divided into non-rapid-eye-movement (NREM) sleep and rapid-eye-movement (REM) sleep based on the EEG signal [39].…”

Section: Introductionmentioning

confidence: 99%

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

Zhang

Liu

et al. 2017

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The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

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“…The general evidence is that lower levels of vigilance are related to increases in lower frequencies (theta and alpha) in EEG spectrum [62]. Several BCI systems have been designed based on this idea [56][57] [63][64]. Interestingly, these studies show that mental (de)activation may be monitored by changing balance between brain activity regions.…”

Section: Eeg and Vigilancementioning

confidence: 99%

“…Several studies have demonstrated the suitability of EEG for real-world monitoring of mental states [55] and for brain-computerinterface (BCI) applications [56][57].…”

Section: Eeg and Vigilancementioning

confidence: 99%