EEG Driving Fatigue Detection With PDC-Based Brain Functional Network

Wang, Fei; Wu, Shichao; Ping, Jingyu; Xu, Zongfeng; Chen, Hao

doi:10.1109/jsen.2021.3058658

Cited by 39 publications

(19 citation statements)

References 46 publications

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“…During the past few years, many studies have focused on driving fatigue from the perspective of complex networks based on EEG signals. Some of these studies [ 11 , 17 , 21 ] combined machine learning to identify the brain states of the driver, which are practical for online fatigue monitoring. Meanwhile, this study’s method was an offline analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The clustering coefficient is a commonly used small-world metric to describe the local aggregation ability of a network [ 21 ]. It refers to the probability that the adjacents of a node also connect with each other.…”

Section: Methodsmentioning

confidence: 99%

“…Complex networks, which can well explain changes in functional connectivity in the brain, have been successfully applied in the study of neurological diseases such as epilepsy and Alzheimer’s disease [ 15 , 16 ]. In the past decade, a lot of research on driving fatigue has also adopted complex network theory [ 17 , 18 , 19 , 20 , 21 ]. However, in most studies, undirected complex networks are mainly used.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, combining EEGs with complex networks to study changes in the brain functions of drivers may provide some objective and effective characteristic indicators and mechanism explanations for driving fatigue. Among them, Granger causality analysis of multivariate time series is widely used in the construction of EEG-based directed networks [ 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

Qin

Chen

et al. 2022

Entropy

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Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

Qin

Chen

et al. 2022

Entropy

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“…At present, the mutlifractality method is widely used in physiological signal processing [33][34][35]. Additionally, as a non-linear processing method, entropy is also widely used in the detection of driving fatigue [36][37][38]. Compared with the mutlifractality method [39,40], MMSE can extract EEG features on a time scale, and the data length required by the MMSE method is shorter in terms of EEG processing.…”

Section: Introductionmentioning

confidence: 99%

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

Wang

Lü

Kang

et al. 2021

Entropy

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The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.

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