Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network

Li, Gang; Xu, Yanting; Jiang, Yonghua; Jiao, Weidong; Xu, Wanxiu; Zhang, Jianhua

doi:10.1155/2020/8825547

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…By aiming to evaluate the brain fatigue state, Li et al analyzed the influence of mental fatigue on the fractal dimension of the brain function network. ey found that the increase in mental fatigue was positively correlated with the fractal dimension, while the fractal dimension of the weighted network was more sensitive to mental fatigue [28]. ese results can support the research results in the present work.…”

Section: Mental Fatigue Evaluation Based On Binary Networksupporting

confidence: 90%

[Retracted] Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear Analysis

Chen

Lai

2021

Complexity

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The purpose of this research is to explore the optimization and fusion application of multimodal neuroimaging technology and analyze the evaluation method of human brain fatigue based on multimodal neuroimaging technology. Based on electroencephalogram (EEG) and fMRI (functional magnetic resonance imaging), the four-dimensional consistency of local neural activities (FOCA) and local multimodal serial analysis (LMSA) are first introduced to fuse EEG and fMRI organically. Second, the eigenspace maximal information canonical correlation analysis (emiCCA) is introduced to construct the multimodal neuroimaging data fusion system. Finally, how the brain function network is constructed is introduced. Based on the binary and the weighted brain function networks, the relationship between the human brain fatigue and the brain function network is evaluated by calculating the fractal dimension. Results demonstrate that FOCA performs well in temporal and spatial consistency indexes, and the mean level and standard deviation in the case of temporal and spatial consistency are approximately 0.45. The effect of LMSA indexes is significantly better than generalized linear models (GLMs). Under different signal-to-noise ratios (SNRs), the regression coefficient based on LMSA is much larger than the GLM estimate; the corresponding significance level is p < 0.05 ; and the maximum value of the regression coefficient appears near 0.2. In the data fusion system, the time-space matching has good results under the time accuracy based on EEG and the space accuracy based on fMRI, with the time accuracy above 88% and the space accuracy above 89%. The fractal dimension analysis based on the brain function network reveals that the weighted brain function network is more sensitive to mental fatigue. The state of human brain fatigue will make the brain function network more complicated. The fractal dimension with more network edges is around 2.2, while the fractal dimension with fewer network edges is around 1.6. The proposed data analysis and fusion system have great application potential and propose a new idea for analyzing human brain fatigue and brain aging.

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Section: Mental Fatigue Evaluation Based On Binary Networksupporting

confidence: 90%

[Retracted] Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear Analysis

Chen

Lai

2021

Complexity

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“…Network features can reflect the functional integration and local aggregation of information between brain regions when performing mental tasks [ 32 , 45 , 47 ]. In this study, we analyzed brain network reorganization during task switching before and after mental fatigue.…”

Section: Discussionmentioning

confidence: 99%

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue

Zhong

Wang

et al. 2022

Sensors

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Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.

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“…Li et al used EEG data on human mental fatigue to construct a brain function network, and proposed a modified greedy coloring algorithm to calculate the fractal dimension of binary and weighted brain function networks. The results showed that the fractal dimension increased with the increase of mental fatigue [20]. Mental fatigue characteristics in some brain leads are not obvious, and the noise interference of redundant channels increases the computational complexity.…”

Section: Introductionmentioning

confidence: 97%

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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Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network

Cited by 5 publications

References 38 publications

[Retracted] Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear Analysis

[Retracted] Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear Analysis

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue

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

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