Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals

Sun, Xiaozhou; Yang, Yaru; Che, Yanqiu; Qin, Yingmei

doi:10.3390/e21040353

Cited by 44 publications

(28 citation statements)

References 47 publications

(81 reference statements)

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“…Different patterns of connectivity between the right and left hemispheres in sensorimotor areas have also been demonstrated during a state of fatigue [201], similar to the findings of Liu et al [200] in different brain regions. In addition, some studies have observed denser functional connectivity during post-fatigued tasks in comparison with pre-fatigued tasks, indicating that the human brain exhibits stronger coupling during fatigue to maintain information transmission until the required task is accomplished [56], [202]- [206]. A higher phase coherence for the alpha and theta bands [205] and a higher PLI for the delta band [206] have been demonstrated during drowsiness in comparison with a state of alertness, indicating a lower degree of asymmetry in the phase difference.…”

Section: A Connectivity Studies On Fatiguementioning

confidence: 99%

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail

Karwowski

2020

IEEE Access

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Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns. The computation of graph theory metrics enables the characterization of the stationary behavior of electroencephalogram (EEG) signals that cannot be explained by simple linear methods. The main purpose of this study was to systematically review the graph theory applications for mapping the functional connectivity of the EEG data in neuroergonomics. Moreover, this article proposes a pipeline for constructing an unweighted functional brain network from EEG data using both source and sensor methods. Out of 57 articles, our results show that graph theory metrics used to characterize EEG data have attracted increasing attention since 2006, with the highest frequency of publications in 2018. Most studies have focused on cognitive tasks in comparison with motor tasks. The mean phase coherence method, based on the "phase-locking value," was the most frequently used functional estimation technique in the reviewed studies. Furthermore, the unweighted functional brain network has received substantially more attention in the literature than the weighted network. The global clustering coefficient and characteristic path length were the most prevalent metrics for differentiating between global integration and local segregation, and the small-worldness property emerged as a compelling metric for the characterization of information processing. This review provides insight into the use of graph theory metrics to model functional brain connectivity in the context of neuroergonomics research.

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Section: A Connectivity Studies On Fatiguementioning

confidence: 99%

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail

Karwowski

2020

IEEE Access

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“…The EEG signal contains information from a complex and dense network of billions of interconnected neurons. In order to capture the inter-regional interactions, recent studies have utilized functional connectivity with graph theory analysis (GTA) metrics to assess driving drowsiness and mental fatigue [42,43]. In the context of GTA, brain networks can be interpreted as a graph, with different anatomical and/or functional brain regions represented by nodes and any interaction represented by links between each pair of brain regions.…”

Section: Introductionmentioning

confidence: 99%

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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“…Han, C et al applied complex network theory to study the changes of brain network characteristics under different fatigue states. Their results demonstrated under fatigue state, the brain network complexity of 16 subjects improved [ 15 ]. Zhao, C used the graph theory method to study the functional network reconstruction changes in different frequency bands of six subjects and compared the brain function network in normal and fatigue states [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

Liu

Yang

Chen

et al. 2021

Entropy

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With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don’t achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy.

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Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals

Cited by 44 publications

References 47 publications

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States

Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

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