Research And Implementation Of Brain Control System By Integrating Signal Between Motor Imagery EEG And EOG

With the development of Brain Computer Interface(BCI)technology,BCI control system attracts extensive attention of researchers,and its performance in the auxiliary equipment for the disabled is outstanding.In BCI system,Motor Imagery(MI)is favored by researchers for its spontaneity.Electrooculography(EOG)is an artifact compared with Electroencephalograph(EEG),but EOG has the advantages of signal stability and easy identification.By adding EOG into the BCI system based on MI,it can further increase the control instructions of the BCI system by fusing EOG and EEG,increase the friendliness of the system operation and better meet the requirements of BCI system application.Based on this,the features of MI and EOG are studied in this paper,then the BCI robot arm control system is designed and implemented by the synchronous combined control of MI and EOG.Firstly,according to the specific theory and requirement of MI,experimental paradigm of EEG and EOG are designed and implemented.and the EOG signal was firstldy processed and analyzed.A total of twelve healthy subjects were collected EEG and EOG data.After down-sampling and filtering,this paper obtained the peak threshold by comparing the peak values of EOG waveform between conscious blink and unconscious blink.For EOG different frequencies,this paper identifies them by detecting the number of wave peaks.The EOG recognition rates are above 80% in the online experiment.Secondly,this paper proposes a feature extraction method of LWT-CSP by studying the common spatial pattern(CSP)algorithm.EEG improve signal-to-noise ratio after a series of operations such as down-sampling,filtered,standardized and sliced.The wavelet coefficients of Mu and Beta rhythms are extracted by LWTand then the CSP is used to compute the spatial feature vector of wavelet coefficients.Finally,the support vector machines(SVM)is used to classify EEG data of MI.For the offline experiment,the average classification accuracies of all subjects reached 75.9%,and the average Kappa value reached 0.51 which shows moderate consistency.Finally,a robot arm control system based on Arduino is designed by combining MI and EOG.In this system,EEG of MI is used to control the steering of robot arm,double EOG is as a task switch,and single EOG is as the other switch to switch different control stages during tasks.In this paper,the BCI robot arm control system is used in a drug taking experiment.After verification,the subjects with high accuracy in the online experiment of motor imagine are selected for the robot arm control experiment.In the control experiment,the subjects control the movement of the robot arm by EEG and EOG,and successfully complete the process of grabbing,taking medicine and recovering medicine bottle.