Study on Brain Electromyography Rehabilitation System Based on Data Fusion and Virtual Rehabilitation Simulation

In this paper, a virtual rehabilitation training system based on electroencephalogram (EMG) feedback is proposed to solve the problem that the existing virtual rehabilitation training methods can not reflect the initiative of patients and lack of individual adaptability. Aiming at the EEG data fusion rehabilitation system based on Virtual Prototyping technology, a motion pattern recognition method based on the feature fusion of EEG and EMG signals is studied to improve the accuracy and flexibility of the rehabilitation training system. The corresponding virtual rehabilitation training scene is designed, and the control and feedback adjustment of the virtual scene are realized by using the above EEG feature analysis method. Finally, the virtual rehabilitation training is realized. The multi-level coupling relationship between EEG and EMG signals under different grip forces was explored by using the synchronous coupling characteristics between EEG and EMG signals, which provided a theoretical basis for further application in clinical rehabilitation evaluation.