A VR-Based Motor Imagery Training System with EMG-Based Real-Time Feedback for Post-Stroke Rehabilitation

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1
• Main Authors: Lin, Meiai, Huang, Jianli, Fu, Jianming, Sun, Ya, Fang, Qiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer applications; Correlation analysis; EEG; Effectiveness; Electrodes; Electroencephalography; Electroencephalography - methods; Electromyography; EMG; Feedback; Frequencies; Humans; Imagery; Imagery, Psychotherapy - methods; Mental task performance; Motor imagery; Muscle strength; Muscles; Real time; real-time feedback; Real-time systems; Recovery; Rehabilitation; Stroke; Stroke (medical condition); Stroke Rehabilitation - methods; Synchronization; Training; Virtual environments; Virtual Reality
• DOI: 10.1109/TNSRE.2022.3210258
• ISSN: 1534-4320; 1558-0210

Rehabilitation is essential for post-stroke body function recovery. Supported by the mirror neuron theory, motor imagery (MI) has been proposed as a potential stroke therapy capable of facilitating the rehabilitation. However, it is often quite difficult to estimate the degree of the participation of patients during traditional MI training as well as difficult to evaluate the efficacy of MI based rehabilitation methods. The goal of this paper is to develop a virtual reality (VR) based MI training system combining electromyography (EMG) based real-time feedback for post-stroke rehabilitation, with the immersive scenario of the VR system providing a shooting basketball training for bilateral upper limbs. Through acquiring electroencephalography (EEG) signal, the brain activity in alpha and beta frequency bands was mapped and the correlation analysis could be achieved. Furthermore, EMG data of each patient was collected and calculated as threshold with root-mean-square algorithm for feedback of the performance score of the shooting basketball training in virtual environment. To investigate the feasibility of this newly-built rehabilitation training system, four experiments namely initial assessment experiment, motor imagery (MI), action observation (AO), and combined motor imagery and action observation (MI+AO) were carried out on stroke patients at different recovery stages. The result shows that MI+AO can generate more pronounced event-related desynchronization (ERD) in alpha band compared to other cases and induce relatively obvious ERD in beta band compared to AO, which demonstrates that VR-based observation has ability to facilitate MI training. Furthermore, it has been found that the muscle strength from MI+AO is the highest through the EMG analysis. This proves that the feedback of EMG can be used to quantify patient's training engagement and promote MI training at a certain extent. Hence, by incorporating such an EMG feedback, a VR-based MI training system has the potential to achieve higher efficacy for post-stroke rehabilitation.