Research On Noise And Vibration Experience Of Automobile Cockpit Based On Brain-Computer Interface

In recent years,more and more vehicle enterprises have gradually turned their focus of development on the field of intelligence with the continuous development of 5G technology.As an interface of direct interaction with passenger,the intelligent cockpit’s basic NVH characteristics directly determine the quality of drivers’ cockpit driving experience.At present,the evaluation of automobile cockpit experience is mainly based on the subjective evaluation score of the subjects.Due to the lack of corresponding standard indicators and evaluation basis,the results are mainly based on the personal feelings of the subjects.However,as a new technology,the brain-computer interface(BCI)acquires EEG data from spontaneous biological potential changes in the brain,which can record and describe the brain activities of subjects.The results are relatively objective,accurate and more reliable.Therefore,based on the BCI method,this thesis aims to study the noise and vibration experience of drivers’ automobile cockpit,and the following studies are conducted.First of all,a vehicle simulation experiment platform with noise stimulation module and vibration stimulation module is designed to simulate the driving situation.Moreover,an experimental paradigm for a cognitive switching task is designed to simulate the brain activity of drivers during driving.At the same time,a subjective evaluation scale is designed to make a comprehensive subjective evaluation of drivers’ driving experience.In this thesis,11 subjects participated in the experiment and completed the EEG data collection.Secondly,the result of the subjective evaluation scale has been analyzed in this thesis.After completing the quantification of the scale and verifying the validity of the data,the regression equation between the comfort level and different stimuli has been established.Through this equation,it has been found that noise had the greatest effect on the comfort level of the subjects with a positive correlation,and vibration had less effect with a negative correlation.After analyzing the subjects’ response time in cognitive switching task,it has been found that vibratory stimuli helped to enhance subjects’ attention and that subjects’ response times raised with increasing fatigue.Finally,this thesis uses the theory of graph to analyze the collected EEG data after data pretreatment.The clustering coefficient,the local efficiency,the characteristic path length,and the global efficiency of brain network parameters have been calculated after EEG topography generating and data correcting.Compared with the resting state data,it has been found that appropriate noise stimulation had a positive effect on the parameters of the brain network.In contrast,vibration stimulation had a negative effect,and the noise had more significant effect on the parameters of the brain network than the vibration.The results of above are consistent with the conclusions obtained from subjective evaluation and mutually prove the accuracy and validity of the conclusions.In addition,by comparing the brain network parameters of different subjects,it has been found that the channels of FP1,AF7,AF8,F7,F8,and FT7 would change significantly under stimulation,which provided feature nodes for reference in subsequent indepth analysis.