PROPERTY OF EXPERIMENTSThe object is mandate to select, reach and clutchobjects in the lectern and deliver them to the actualposition. This order will be ignored by the system ifthere is motor imagery state is diagnose from the EEGsignals while there is no object is selected . Once thereaching exertion is activated successfully that the leerpoint is over the object through motor imagery state,then the robotic arm will transfer to the specificposition eventually. The crack of the hook will becontrolled manually by the user. The gripper is open inthe first state and the crack of the hook will decrease1mm each step in avaricious methods if the usersustain motor imagery state and possessed on the userin the image plan. Cracked information is delineated todegrees of the navigation as the controlling command.Further motor imagery states untangled from BMI isneeded to inaugurate the action for objects shipmentand then user will be moved towards the target cap selfexecuting the object will be released finally when theEmotive has identified motor imagery state and therobotic arms returns towards its position. Avariciousprocedure in open loop is accomplished for contrastwith the method above. For the open-loop controlprotocol, by visual exploration only, the user candecides how to stop the rapacious procedure. In thisprocedure, the task is repeated 30 times, 15 times withAR feedback or 15 times through visual exploration.To indices are used to estimate the performance of thesystem, (1) the time used in the avaricious procedure(2) the controlling flaw rate of gripper crack, It is usedfor such deliberation when the user is already seizedthroughout the object still preserve the motor imagery stateand focused on the object, the robotic arm will continueimplement the charge. In this paper, we had recruit themotor imagination pattern to control the robot arm joints.Human beings are not sensitive for the judgments of thespatial aspect, it is more scathing problem to accuratelymould the angle of a space. For collecting EEG signals, weuse German Brain Company’s actichamp series amplifier.The sensory cortex section were used which is covered bysixteen electrodes impedance dominance in 10 k, the signalinspect rate is 200 Hz. The composed signal is processedalong 8-45 Hz band-pass filter.? EXPERIMENT AND SEQUELThe procedure accommodates On-line experiments andoff-line experiments. To manufacture classifier andoptimal framework, we used off-line experiments. Thefocus of assess the efficacy of our overall system is onlineexperiment. The prosecution time for the graspingAR result is usually shorter than that without AR. Whenthere is no feedback of AR it is too much difficult for theobject to distinctly perceive the state of grasping methodfrom one panorama. With AR feedback, the condition ofthe avaricious method and the reproduce grasping forceinto the gripper and object. That’s why; the user can easilyhandle this procedure. The result have unveiled that thetask were finished in 20s with a standard deviation lessthen 0.4s. there are 3 groups in off-line trainingexperiment. There are 90 trials for the left right hand MIin each group and idle state. In this experiment to inducethe current task, the screen monitor is used. When the usersees an induced text “left” or “right” on the screen theuser should quickly assume the movement of theequivalent hand without performing such task. The usershould be relaxed when induced ‘rest’ on the screenwithout any performance. In the experiments, the firstposition and the target position are identified UN orderly.As stated by, the object needs to dominance the robot armto the position by EMG and EEG, then clutch the bottleand then pass it to the target position.? CONCLUSIONThe focal bequest of this paper is that a closed-loopdominance system through integrating hybrid BMIwith AR interface is propound for thegrasping task. In this system, Eye tracking, volitionalcontrol, automatic control or AR feedback aremerged to reduced object’s burden. In this study, wepresent a robot arm control procedure throughcombining EMG and EEG signals. EEG signals areexact and rapid response. Brain-computer interfaceform on motion imagination can separately regulateoutput command which is perfect/suitable for uttertime control. The procedure propound in this paperhas completely deploy the characteristics of thesetwo of signals. Two kinds of signals and thereforewe obtained good results. In both experiments, theconclusion demonstrated the procedure is friendlyuse and effective. While, this program still has somefaults. That is the system delicate to the motorimagery pattern as user in this pattern require longterm training could able to attain high accuracy.That’s why, we propound largely spread BCI patternfor this method of robot arm control. The problemwe can face in this experiment is that, the highdegree freedom of the manipulator essential tocomplicated motion planning. There is lot of wastageof time. In this experiment, because of inadequacy ofnecessary adaptation and training the users of therobot arm motion training in nor skilled. Although,this problem can be managed through training. Theperformance of the propound system will becompared with many kinds of feedback interface. Iwhich, haptic feedback and auditory feedback areincluded and so on.