Immersive AR Merged with MI-BCI Hand Function Rehabilitation Training System for Stroke Patients

Abstract

Strokes can cause neurological damage to the patient, which leads to hand dysfunction. Traditional methods of hand function rehabilitation, such as electrical stimulation and therapist-dependent movement therapy, are ineffective due to the brain's lack of direct involvement in the motor nervous system. To improve the rehabilitation efficacy, we design a rehabilitation system based on motor imagery brain-computer interface (MI-BCI) and augmented reality (AR) for hand function rehabilitation of stroke patients. It includes two-class motor imagery tasks: left-hand fist and right-hand fist based on AR. Motor imagery electroencephalogram (MI-EEG) is acquired from 10 subjects and decoded by using an algorithm module encapsulated in the master system. It reaches an average accuracy of 76.4% and is eventually fed back to patients through rehabilitation peripherals. In addition, the master system provides an interactive interface with features to design treatment tasks, manage patient information and monitor patient status. The system realizes an immersive rehabilitation experience that promotes the reconstruction of the central nervous system and provides a new approach for stroke patients to recover.