Analysis The Mechanism Of Pedal Misoperation In Drivers By Effective Connectivity Based On Near-infrared Spectroscopy

At present,the traffic accident,known as "the first public hazard in the world",remains a serious public health problem.It seriously affects the national economy and social life.Therefore,it is very important to pay attention to the road safety.Effective measures based on the analyses of the causes and occurrence rules of traffic accidents should be taken to fundamentally reduce or eliminate the hidden dangers of traffic accidents.Drivers,as a dynamic factor,are the main body of traffic safety.Their conditions directly affect the efficiency,stability and safety of driving.How to prevent and control the traffic accidents from the view of driver has become a common concern.This paper aimed to investigate the brain function of drivers during different task states,explore the relationship between the brain functional network and driving behavior,and explain the neural mechanism of pedal misoperation based on the combination between neuroscience and ergonomics.This study will guide more efficient and safer driving behavior,and further provide theoretical basis for safety and pleasant design in the vehicle.In this study,functional near-infrared spectroscopy(fNIRS)was employed to measure the cerebral oxygenated hemoglobin(delta HbO2)in the bilateral prefrontal cortices,motor cortices,and occipital lobes of the drivers during different driving tasks.Specific preprocess methods were used to remove the interference components and improve the accuracy of the signals.A coupled-phase-oscillator model was established based on the dynamic phase information extracted by wavelet transform in different frequency intervals.Dynamic Bayesian inference(DBI)was used to obtain the best parameter set for describing the coupling model.Based on the coupling function and DBI,the driver's cortical effective connectivity(EC)model was established to quantitatively analyze the brain functional network in drivers during different driving task.Firstly,this study aimed to describe the characteristics of EC network of the drivers during different driving conditions.Based on the normal driving task,the virtual driver increases the mental load of drivers by designing different driving risk points,and trigger the drivers to step on the wrong pedal.A total of 12 young drivers were recruited to perform the resting state,normal driving task and complex driving task on the simulator.Thirty-six channels of fNIRS were used to measure the delta HbO2 signals of the drivers.The driving behavior data was recorded simultaneously during driving.Based on coupling function and DBI,the cortical EC model was constructed to analyze the change in functional network of drivers during different driving tasks.Results show that driving tasks can induce increased activation in different functional areas of drivers in a varying degree.The regulation function from prefrontal areas to motor and occipital lobes was significantly increased during the driving tasks compared with resting state.The increased complexity of driving task reduced the regulatory function between motor areas and from prefrontal lobe exerted on motor area.In addition,these changes were positively correlated with driving stability.Secondly,this paper aimed to analyze the gender-related differences in cerebral functional network under different driving tasks,and explore the influence of gender factors on cerebral mechanism underlying pedal misoperation.In this study,a 14-channel fNIRS was used to collect the delta HbO2 signals of 17 male and 13 female drivers in the resting state,normal driving task and complex driving task.The information of driving behavior was simultaneously recorded.DBI was applied to calculated the coupling strength and coupling direction according to the channel-wise coupled-phase-oscillator model.Results show that drivers of different genders generate functional reorganization in network in a varying degree according to the difficulty of driving tasks.The complex driving task of this study has different effects on the driving stability of drivers with different genders.Furthermore,the significant correlation between cortical EC and pedal misoperation behavior is reflected in the different brain regions according to the gender difference.In summary,the study firstly proved the validity of fNIRS-based cortical effective connectivity model in evaluating the cerebral state of drivers.The dynamic coupling function between the motor areas of the drivers can be used to evaluate the driver's mental load and predict driving stability.In the human-computer interaction system.this study can detect and alert the driver's brain load status in advance,effectively remind the driver whether there is a risk of pedal misoperation to prevent traffic accidents and improve road safety.At the same time,analysis of cerebral functional mechanism can provide important reference for judging driver's driving ability and optimizing the design of interface in human-computer interaction and driver-assistance system.