Brain Machine Interface with Closed-Loop Neuromuscular Stimulation for Grasping in Stroke and Spinal Cord Injury Survivors



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Sixty percent of elderly hand movements involve grasping, which is unarguably why grasp restoration is a major component of upper-limb rehabilitation therapy. Neuromuscular, or functional electrical stimulation (FES), can help retrain grasping by using short bursts of electrical pulses to artificially contract paralyzed muscles. However, current home-use FES requires users to operate a keypad or coordinate body movements for initiating the stimulation, which is often challenging and inefficient for paralyzed patients, as well as unfeasible for severely impaired patients. Conversely, therapeutic FES devices that are controlled by a therapist or pre-programmed, fail to engage patients and ultimately undermine the therapy outcomes. Besides, commercially available FES devices are open-loop systems that require frequent parameter adjustments, which disrupts their continuous use.

To increase engagement and ensure accessibility to severely impaired patients, several researchers have suggested non-invasive electroencephalography (EEG)-based brain-machine interfaces (BMI) that allow patients to operate FES devices using their brain activity. However, EEG’s weak signal-to-noise ratio and inherent trial-to-trial variability, have deteriorated the performance of EEG-based BMIs and compromised their long-term reliability. Likewise, closed-loop FES for grasping is promising, but its long-term efficacy is debatable due to lack of effective muscle models, subject variability, and implementation challenges.

To address EEG’s challenges, we developed a novel BMI design using optimal adaptive windows to extract movement related cortical potentials, which is a widely studied neural correlate of movement intention. A pilot study with four chronic stroke survivors demonstrated consistent above chance-level BMI performance (65% true and 28% false positives) across two days. In a subsequent study involving two stroke, one spinal injury, and two control subjects, we evaluated the efficacy of integrating BMI with closed-loop FES in order to restore grasping. A custom-built FES prototype using feedback control was developed to automatically adjust the stimulation intensities during grasping and was further validated in an isometric force tracking task. After three sessions, it was concluded that the normalized tracking errors were significantly smaller during closed-loop stimulation (25 ± 15%) versus open-loop stimulation (31 ± 24%), (F (748.03, 1) = 23.22, p < 0.001). These findings will benefit future designs of BMI with closed-loop FES and help determine the clinical efficacy of BMI-FES therapy in motor rehabilitation, following stroke or spinal cord injury.



Brain-machine interface, Neuromuscular electrical stimulation, Neurosciences, Functional electrical stimulation, Closed-loop, Stroke, Spinal cord injuries, Rehabilitation