A State-Space Control Approach for Tracking Isometric Grip Force During BMI Enabled Neuromuscular Stimulation

Sixty percent of elderly hand movements involve grasping, which is unarguably why grasp restoration is a major component of upper-limb rehabilitation therapy. Neuromuscular electrical stimulation is effective in assisting grasping, but challenges around patient engagement and control, as well as poor movement regulation due to fatigue and muscle nonlinearity continue to hinder its adoption for clinical applications. In this study, we integrate an electroencephalography-based brain-machine interface (BMI) with closed-loop neuromuscular stimulation to restore grasping and evaluate its performance using an isometric force tracking task. After three sessions, it was concluded that the normalized tracking error during closed-loop stimulation using a state-space feedback controller (25 +/- 15%), was significantly smaller than conventional open-loop stimulation (31 +/- 24%), (F (748.03, 1) = 23.22, p < 0.001). Also, the impaired study participants were able to achieve a BMI classification accuracy of 65 +/- 10% while able-bodied participants achieved 57 +/- 18% accuracy, which suggests the proposed closed-loop system is more capable of engaging patients for rehabilitation. These findings demonstrate the multisession performance of model-based feedback-controlled stimulation, without requiring frequent reconfiguration.