Objective. Non-invasive, high-density electromyography (HD-EMG) has emerged as a useful tool to collect a range of neurophysiological motor information. Recent studies have demonstrated changes in EMG features that occur after stroke, which correlate with functional ability, highlighting their potential use as biomarkers. However, previous studies have largely explored these EMG features in isolation with individual electrodes to assess gross movements, limiting their potential clinical utility. This study aims to predict hand function of stroke survivors by combining interpretable features extracted from a wearable HD-EMG forearm sleeve. Approach. Here, able-bodied (N = 7) and chronic stroke subjects (N = 7) performed 12 functional hand and wrist movements while HD-EMG was recorded using a wearable sleeve. A variety of HD-EMG features, or views, were decomposed to assess alterations in motor coordination. Main Results. Stroke subjects, on average, had higher co-contraction and reduced muscle coupling when attempting to open their hand and actuate their thumb. Additionally, muscle synergies decomposed in the stroke population were relatively preserved, with a large spatial overlap in composition of matched synergies. Alterations in synergy composition demonstrated reduced coupling between digit extensors and muscles that actuate the thumb, as well as an increase in flexor activity in the stroke group. Average synergy activations during movements revealed differences in coordination, highlighting overactivation of antagonist muscles and compensatory strategies. When combining co-contraction and muscle synergy features, the first principal component was strongly correlated with upper-extremity Fugl Meyer hand sub-score of stroke participants (R2 = 0.86). Principal component embeddings of individual features revealed interpretable measures of motor coordination and muscle coupling alterations. Significance. These results demonstrate the feasibility of predicting motor function through features decomposed from a wearable HD-EMG sleeve, which could be leveraged to improve stroke research and clinical care.

Abstract Objective Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs. Approach To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations. Main results Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve’s design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort. Significance The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.

Impairments in somatosensory function are a common and often debilitating consequence of neurological injury, with few effective interventions. Building on success in rehabilitation for motor dysfunction, the delivery of vagus nerve stimulation (VNS) combined with tactile rehabilitation has emerged as a potential approach to enhance recovery of somatosensation. In order to maximize the effectiveness of VNS therapy and promote translation to clinical implementation, we sought to optimize the stimulation paradigm and identify neural mechanisms that underlie VNS-dependent recovery. To do so, we characterized the effect of tactile rehabilitation combined with VNS across a range of stimulation intensities on recovery of somatosensory function in a rat model of chronic sensory loss in the forelimb. Consistent with previous studies in other applications, we find that moderate intensity VNS yields the most effective restoration of somatosensation, and both lower and higher VNS intensities fail to enhance recovery compared to rehabilitation without VNS. We next used the optimized, moderate intensity to evaluate the mechanisms that underlie recovery. We find that moderate intensity VNS enhances transcription of Arc, a canonical mediator of synaptic plasticity, in the cortex, and that transcript levels were correlated with the degree of somatosensory recovery. Moreover, we observe that blocking plasticity by depleting acetylcholine in the cortex prevents the VNS-dependent enhancement of somatosensory recovery. Collectively, these findings identify neural mechanisms that subserve VNS-dependent somatosensation recovery and provide a basis for selecting optimal stimulation parameters in order to facilitate translation of this potential intervention.

High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 +/- 0.3%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.3 \pm 0.3\%$$\end{document} accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 +/- 0.8%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$92.8 \pm 0.8\%$$\end{document}. When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 +/- 0.9%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93.3 \pm 0.9\%$$\end{document} on the sequential set and 88.5 +/- 0.9%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$88.5 \pm 0.9\%$$\end{document} on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>2$$\end{document} of 0.884 +/- 0.003\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.884 \pm 0.003$$\end{document} in the sequential set and 0.750 +/- 0.008\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.750 \pm 0.008$$\end{document} in the mixed set. Median absolute error (MAE) was kept below 10 degrees across all joints, with a grand average MAE of 1.8 +/- 0.04 degrees\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.8 \pm 0. 04<^>\circ$$\end{document} and 3.4 +/- 0.07 degrees\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.4 \pm 0.07<^>\circ$$\end{document} for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human-robot collaboration.

AbstractBackgroundFunctional electrical stimulation (FES) has been recognized for decades as a method to retrain the motor system after stroke. Benefits of FES rehabilitation can be enhanced by combining task-oriented therapy, dubbed FES therapy (FEST). Furthermore, by synchronizing FES with the user’s volitional motor intention and incorporating multiple trained tasks FES can be better integrated into common task-oriented rehabilitation practice. Using wearable FES technology, we tested therapy incorporating these elements in two chronic stroke survivors.MethodsOur group has developed the NeuroLife® Sleeve, a wearable forearm sleeve that contains a high-density grid of embedded FES electrodes, that may be controlled by an operator or by the wearer’s own electromyographic (EMG) signals. During eight weeks of FEST, intention-driven FES enabling multiple movements was delivered via operator control twice weekly and EMG control once weekly.ResultsAt the end of the therapy period, subjects A and B had both improved their scores: Box and Blocks Test (A: +5, B: +7), the Action Arm Research Test (A: +7, B: +12), the Fugl Meyer Upper Extremity section (A: +11, B: +9), and the 9-Hole Peg Test (A: 158 sec, B: 54 sec, both previously unable). All score improvements persisted over the 10-week follow-up period despite greatly reduced (>80%) effective dose of FES.ConclusionsThis case series provides additional evidence that intention-driven FEST drives long-lasting motor recovery in chronic stroke survivors. The NeuroLife Sleeve enabled this therapy through the easily donned wearable sleeve interface, control schemes for pairing FES with motor intention, and efficient transitions between tasks with programmable FES placement and parameters.

Loss of sensory function is a common consequence of neurological injury. Recent clinical and preclinical evidence indicates vagus nerve stimulation (VNS) paired with tactile rehabilitation, consisting of delivery of a variety of mechanical stimuli to the hyposensitive skin surface, yields substantial and long-lasting recovery of somatosensory function after median and ulnar nerve transection and repair. Here, we tested the hypothesis that a specific component of the tactile rehabilitation paired with VNS is necessary for recovery of somatosensory function. In a second experiment in a separate cohort, we investigated whether VNS paired with tactile rehabilitation could improve skilled forelimb motor function. Elements of the study design, including planned sample size, assessments, and statistical comparisons, were preregistered prior to beginning data collection (https://osf.io/3tm8u/). Animals received a peripheral nerve injury (PNI) causing chronic sensory loss. Eight weeks after injury, animals were given a VNS implant followed by six weeks of tactile rehabilitation sessions consisting of repeated application of one of two distinct mechanical stimuli, a filament or a paintbrush, to the previously denervated forepaw. VNS paired with either filament indentation or brushing of the paw significantly improved recovery of forelimb withdrawal thresholds after PNI compared to tactile rehabilitation without VNS. The effect size was twice as large when VNS was paired with brushing compared to VNS paired with point indentation. An independent replication in a second cohort confirmed that VNS paired with brush restored forelimb withdrawal thresholds to normal. These rats displayed significant improvements in performance on a skilled forelimb task compared to rats that did not receive VNS. These findings support the utility of pairing VNS with tactile rehabilitation to improve recovery of somatosensory and motor function after neurological injury. Additionally, this study demonstrates that the sensory characteristics of the rehabilitation paired with VNS determine the degree of recovery.

Closed-loop vagus nerve stimulation (VNS) paired with rehabilitative training has emerged as a strategy to enhance recovery after neurological injury. Previous studies demonstrate that brief bursts of closed-loop VNS paired with rehabilitative training substantially improve recovery of forelimb motor function in models of unilateral and bilateral contusive spinal cord injury (SCI) at spinal level C5/6. While these findings provide initial evidence of the utility of VNS for SCI, the injury model used in these studies spares the majority of alpha motor neurons originating in C7-T1 that innervate distal forelimb muscles. Because the clinical manifestation of SCI in many patients involves damage at these levels, it is important to define whether damage to the distal forelimb motor neuron pools limits VNS-dependent recovery. In this study, we assessed recovery of forelimb function in rats that received a bilateral incomplete contusive SCI at C7/8 and underwent extensive rehabilitative training with or without paired VNS. The study design, including planned sample size, assessments, and statistical comparisons, was preregistered prior to beginning data collection ( https://osf.io/ysvgf/ ). VNS paired with rehabilitative training significantly improved recovery of volitional forelimb strength compared to equivalent rehabilitative training without VNS. Additionally, VNS-dependent enhancement of recovery generalized to 2 similar, but untrained, forelimb tasks. These findings indicate that damage to alpha motor neurons does not prevent VNS-dependent enhancement of recovery and provides additional evidence to support the evaluation of closed-loop VNS paired with rehabilitation in patients with incomplete cervical SCI.

Objective Sensory dysfunction is a common consequence of many forms of neurological injury, including stroke and nerve damage. Rehabilitative paradigms that incorporate sensory retraining can provide modest benefits, but the majority of patients are left with lasting sensory loss. We have developed a novel strategy that uses closed‐loop vagus nerve stimulation (VNS) paired with tactile rehabilitation to enhance synaptic plasticity and facilitate recovery of sensory function. Methods A clinical case report provides initial evidence that a similar implementation of closed‐loop VNS paired with a tactile rehabilitation regimen could improve recovery of somatosensory function. Here, we sought to build on these promising initial clinical data and rigorously evaluate the ability of VNS paired with tactile rehabilitation to improve recovery in an animal model of chronic sensory loss. The study design, including planned sample size, assessments, and statistical comparisons, was preregistered prior to beginning data collection ( https://osf.io/xsnj5/ ). Results VNS paired with tactile rehabilitation resulted in a significant and nearly complete recovery of mechanosensory withdrawal thresholds. Equivalent tactile rehabilitation without VNS failed to improve sensory function. This VNS‐dependent restoration of sensory thresholds was maintained for several months after the cessation of stimulation, illustrating long‐term benefits. Moreover, VNS paired with tactile rehabilitation resulted in significant generalized improvements in other measures of sensorimotor forepaw function. Interpretation Given the safety and tolerability of VNS therapy, these findings suggest that incorporating VNS paired with sensory retraining into rehabilitative regimens may represent a fundamentally new method to increase recovery of sensory function after neurological injury. ANN NEUROL 2020;87:194–205

Incomplete recovery of sensory function is common after peripheral nerve injury (PNI). Despite reinnervation following injury, disorganized cortical representations persist and may contribute to functional deficits. There is a dearth of literature characterizing cortical responses after PNI in rodent models. Here we develop a quantitative electrophysiological method for mapping forepaw digit responses in primary somatosensory cortex (S1) of rats. We tested the hypothesis that PNI in the forelimb would generate significant, long lasting sensory deficits, and corresponding disorganization in S1. Rats underwent a transection of the proximal segment of the median and ulnar nerves in the forelimb followed by tubular repair. 4-12 months after nerve injury, we tested mechanosensory withdrawal thresholds and mapped S1 responses to mechanical stimulation of the digits. PNI produces persistent elevation of mechanical withdrawal thresholds, consistent with an impairment in sensory function. Assessment of cortical neurophysiology reveals a substantial disorganization of S1 somatotopy. Additionally, we document degraded timing and digit specificity of cortical responses. This quantitative measurement of long-term changes in S1 digit representations after forelimb nerve injury in rodents provides a framework for further studies focused on the development of therapeutic strategies to restore cortical and sensory function.

Recovery from serious neurological injury requires substantial rewiring of neural circuits. Precisely-timed electrical stimulation could be used to restore corrective feedback mechanisms and promote adaptive plasticity after neurological insult, such as spinal cord injury (SCI) or stroke. This study provides the first evidence that closed-loop vagus nerve stimulation (CLV) based on the synaptic eligibility trace leads to dramatic recovery from the most common forms of SCI. The addition of CLV to rehabilitation promoted substantially more recovery of forelimb function compared to rehabilitation alone following chronic unilateral or bilateral cervical SCI in a rat model. Triggering stimulation on the most successful movements is critical to maximize recovery. CLV enhances recovery by strengthening synaptic connectivity from remaining motor networks to the grasping muscles in the forelimb. The benefits of CLV persist long after the end of stimulation because connectivity in critical neural circuits has been restored.