Design Of Virtual Rehabilitation System Based On Hybrid Brain-Computer Interface

At different stages after stroke,depending on the patient's clinical performance,the requirements for rehabilitation and care are also different.At present,the existing virtual rehabilitation system increases the interest of rehabilitation training,but generally it can only target the patient population in a certain period,that is,the target population is single,and can not meet the individualized rehabilitation needs of patients in different periods,and it is difficult to penetrate the entire rehabilitation process of the patient.In view of the above problems,this paper combines the hybrid brain-computer interface technology with virtual reality and augmented reality technology to design a corresponding rehabilitation training strategy for stroke patients in different periods.A virtual rehabilitation system based on hybrid brain-computer interface is built.The specific work of this paper is as follows:Firstly,for the patients with flaccid paralysis period after stroke,a rehabilitation training strategy based on motor imagery and augmented reality is proposed,which is to identify and locate the limb movement through Kinect,and through the augmented reality technology,the virtual object is added to the real scene,and the patient can realize the control of the virtual object in the real scene through the motion imagination mode,which greatly improves the enthusiasm of the patient's rehabilitation training,and thus can achieve the effect of brain function remodeling.Secondly,for the recovery period patients with stroke,a rehabilitation training strategy based on EMG feedback and Kinect interaction is proposed,which is to perform limb motion recognition and localization through Kinect.Preprocessing,feature extraction and pattern recognition of acquired EMG signals.Through the recognition result,the control function of the virtual scene is completed,and the muscle strength is evaluated by the corresponding index of the EMG signal,thereby realizing the adjustment of the difficulty coefficient of the virtual rehabilitation training,thereby meeting the individualized rehabilitation needs of the patient.In addition,for patients with convulsion period after stroke,a rehabilitation trainingstrategy based on EEG-EMG decision fusion is designed.The recognition results under the two modes of motion imagery and EMG feedback are used to make decision fusion through Bayesian algorithm.More accurate control of the virtual scene to meet the patient's rehabilitation needs.Finally,a virtual rehabilitation system based on hybrid brain-computer interface is designed,including the construction of the system hardware platform and the realization of software functions.Then the experimental research and clinical verification of the three rehabilitation training strategies involved in this paper were carried out respectively.The practicality of the virtual rehabilitation system described in this paper was verified by data comparison analysis before and after the experiment.