虚拟诱导患者下肢主动运动意图及其脑电精准感知方法

Aiming at the problem that the patients with lower limb motor dysfunction cannot generate strong movement active intention, resulting in poor human-machine interaction of exoskeleton robot in rehabilitation motion assistance, a method of virtual induction of patient's lower limb active movement intention and its electroencephalogram (EEG) precise sensing is proposed. Firstly, the factors that affect the generation of patients' motion intention are analyzed, a virtually induced patient intention generation model based on brain attention mechanism is established to form a human-computer interaction strategy based on EEG signals. Secondly, a data-driven immersive three-dimensional virtual induction scene is designed and constructed to stimulate patient's brain to generate active motor intention. Furthermore, EEG signals of patients are collected and pretreated by the artifact removal method based on CEEMDAN-ICA. Finally, a deep convolutional neural network (CNN) is used to accurately identify the patient's movement intention. Experimental results show that the virtual induction method can effectively enhance the characteristics of EEG signals, and the recognition of motion intention is significantly improved. Compared with the conventional method, when the virtual induction method is adopted, the accuracy rate of resting state recognition reaches 80.5%, 10.33% higher than that of the conventional method, and the accuracy rate of generation intention recognition reaches 92.17%, 20.5% higher than that of conventional method, which is stable at a high level. These results lay a foundation for the auxiliary control of exoskeleton robot.