Study On The Neural Mechanism Of Fatigue Driving Based On Brain Network

Fatigue driving is a major cause of traffic accidents and the main cause of major traffic accidents.Long-term mental work of the central nervous system results in energy consumption,which leads to driving fatigue.Meanwhile,the monotony,dullness and frequent repetition of driving behavior can also easily lead to central depression and driving fatigue.Therefore,the study of driver’s brain fatigue and its neurological mechanism during driving is an important topic,which can provide an objective and effective index for the study of fatigue driving,and provide a theoretical basis for the study of the corresponding countermeasures.In this paper,we designed a driving simulation experiment to collect EEG signals.We used power spectrum analysis method,sample entropy analysis method and complex brain network theory to study the changes of EEG signals and the changes of brain network state during different periods and different rhythms of simulated driving.We also discussed the neurological mechanism of brain fatigue induced by long-term simulated driving task.The main contents of this paper are as follows:1.This paper developed an analog driving system V1.0 for vigilance monitoring,and designed a simple,boring and as realistic as possible experiment task to collect EEG signals.2.Studied the changes of power spectrum and sample entropy of EEG signals in different rhythms of T0-T6 during simulated driving and their neural mechanisms.During the simulated driving process,in general,the relative power spectrum of delta rhythm and theta rhythm increases significantly in the central area,the central parietal area,the top area and the occipital area of the mid-posterior or posterior segment of the driving process.For the sample entropy,most of the channels show a downward trend under all rhythms,and begin to decline significantly in the second half of the driving process,especially in the frontal,central,central parietal and apical regions.It shows that with the increase of simulated driving time,the degree of brain fatigue will deepen,the relative power spectrum of EEG signal will increase,the complexity of EEG signal will decrease,and the activity of EEG signal will decrease in frontal central area,central area,central parietal area and parietal area.3.The brain functional network was built based on complex network theory.Pearson correlation was used to measure the functional connectivity between different brain regions,and a series of thresholds were selected to determine the edge connections between network nodes.Finally the brain functional network was constructed.4.Systematically analyzed the changes of network characteristics in the process of driving fatigue,including average path length,average clustering coefficient,global efficiency,local efficiency and degree of centrality.The results show that the average clustering coefficient,global efficiency and local efficiency of alpha,delta and theta rhythms increase and the average path length decreases with the increase of fatigue degree.There is an upward trend in the degree centrality of each node.The alpha and theta rhythms increased significantly in most of the right nodes of the brain,and the delta rhythms increased significantly in most of the nodes.It can be concluded that the regular changes of the brain during fatigue are caused by the synchronization effect between different regions of the brain.In the fatigue state,the brain must activate more functional connections to improve the efficiency of information processing and transmission in the cerebral cortex,so that the brain can successfully complete the simulated driving task,resulting in insufficient energy supply for the brain until sleepiness and alertness decline.