Eeg Analysis And Bci Research Based On Motor Imagery Under Driving Behavior

BCI can provide an auxiliary control mode for the people with physical disabilities, butnormal brain thinking. In this paper, EEG features and control interface system based onmotor imagery under driving behavior are researched. In order to simulate a real driving scene,the experiment designed into the characteristics of the traffic scene and driver’s feelings Thisresearch involves the state of perception of visual, auditory and audio-visual multi-mode, inwhich the related EEG is collected and featured, while the subjects are looked as black boxsystems. In the tips of the traffic information, the subjects imagine the right or left handmovement to the driving behavior to start or brake the vehicle, then the EEG is collected andanalysed.With Neuroscan software, three types of EEG are preprocessed, then the feature isextracted and classified using the CSP algorithm and the linear regression algorithm,respectively. The result is better.Considering the good results of the auditory perception, a BCI system model for drivingis designed with SPEC061a and peripheral systems based on speech recognition technology.The speech system can produce voice command to drive the vehicle, and also can adjust thedriving behavior as a feedback-based on subjects in a timely manner.The main work is as follows:1. Design of virtual traffic environmentA virtual traffic environment is designed by Maya software. Combining of the realistictraffic scenarios, three environments are designed for simultaneous auditory, visual oraudio-visual. The visual design uses a traffic light system, while the auditory mode uses thebrake whistle and audio-visual environment with the integration of both. The design of virtualtraffic environment can give full consideration to the human sensory organs, and also can improve the immersion of subjects which proves a favorable effect on the experiment.2. EEG pretreatmentUsing Neuroscan, the driving EEG is collected and preprocessed, after which the EOG,EMG, and frequency interference are removed. The EEG graph shows the differences inreaction intensity and duration in the three perception mode, while the EEG of auditory modehas a longer duration more than1s, which lays the foundation for further study.3. Feature extractionUsing CSP, the feature of pretreatment data is extraced from the time and frequencydomain. Results: EEG under auditory perception has an obvious feature from the time domain.The signals sampled from C3and C4all shows that the voltage amplitude caused by the rightimagery is higher than the others, the difference between the maximum amplitude is120μV;and the longer duration is similar to the result of EEG graph.4. Classification of EEGUsing linear regression algorithm，the highest average recognition rate is83.3%underthe visual and audio-visual perception, but the rate under auditory perception is96.67%. Thelinear regression algorithm obtains a better result in classifying the data under the auditoryperceptive.5. Design of speech recognition driving modelAccording to the good results obtained from the auditory perceptive, the controllingmodel with moive control and feedback of BCI vehicle is designed using SPCE061a systems.The system is able to achieve the control of vehicle to go forward or stop by motor imagery,which can provide the experimental data for the BCI vehicle.