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Objective. Neural signals in residual muscles of amputated limbs are frequently decoded to control powered prostheses. Yet myoelectric controllers assume muscle activity of residual muscle is similar to that of intact muscle. This study sought to understand potential changes to motor unit (MU) properties after limb amputation. Approach. Six people with unilateral transtibial amputation were recruited. Surface electromyogram (EMG) of residual and intact tibialis anterior (TA) and gastrocnemius (GA) muscles were recorded while subjects traced profiles targeting up to 20 and 35% of maximum activation for each muscle (isometric for intact limbs). EMG was decomposed into groups of motor unit (MU) spike trains. MU recruitment thresholds, action potential amplitudes (MU size), and firing rates were correlated to model Henneman’s size principle, the onion-skin phenomenon, and rate-size associations. Organization (correlation) and modulation (rates of change) of relations were compared between intact and residual muscles. Main results. The residual TA exhibited significantly lower correlation and flatter slopes in the size principle and onion-skin, and each outcome covaried between the MU relations. The residual GA was unaffected for most subjects. Subjects trained prior with myoelectric prostheses had minimally affected slopes in the TA. Rate-size association correlations were preserved, but both residual muscles exhibited flatter decay rates. Significance. We showed peripheral neuromuscular damage also leads to spinal-level functional reorganization. Our findings suggest models of MU recruitment and discharge patterns for residual muscle EMG generation need reparameterization to account for disturbances observed. In the future, tracking MU pool adaptations may also provide a biomarker of neuromuscular control to aid training with myoelectric prostheses.

 

Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks. 

Objective.Transcutaneous electrical stimulation of peripheral nerves is a common technique to assist or rehabilitate impaired muscle activation. However, conventional stimulation paradigms activate nerve fibers synchronously with action potentials time-locked with stimulation pulses. Such synchronous activation limits fine control of muscle force due to synchronized force twitches. Accordingly, we developed a subthreshold high-frequency stimulation waveform with the goal of activating axons asynchronously.Approach.We evaluated our waveform experimentally and through model simulations. During the experiment, we delivered continuous subthreshold pulses at frequencies of 16.67, 12.5, or 10 kHz transcutaneously to the median and ulnar nerves. We obtained high-density electromyographic (EMG) signals and fingertip forces to quantify the axonal activation patterns. We used a conventional 30 Hz stimulation waveform and the associated voluntary muscle activation for comparison. We modeled stimulation of biophysically realistic myelinated mammalian axons using a simplified volume conductor model to solve for extracellular electric potentials. We compared the firing properties under kHz and conventional 30 Hz stimulation.Main results.EMG activity evoked by kHz stimulation showed high entropy values similar to voluntary EMG activity, indicating asynchronous axon firing activity. In contrast, we observed low entropy values in EMG evoked by conventional 30 Hz stimulation. The muscle forces evoked by kHz stimulation also showed more stable force profiles across repeated trials compared with 30 Hz stimulation. Our simulation results provide direct evidence of asynchronous firing patterns across a population of axons in response to kHz frequency stimulation, while 30 Hz stimulation elicited synchronized time-locked responses across the population.Significance.We demonstrate that the continuous subthreshold high-frequency stimulation waveform can elicit asynchronous axon firing patterns, which can lead to finer control of muscle forces.

 

Objective: Haptic perception is an important component of bidirectional human-machine interactions that allow users to better interact with their environment. Artificial haptic sensation along an individual’s hand can be evoked via noninvasive electrical nerve stimulation; however, continuous stimulation can result in adaptation of sensory perception over time. In this study, we sought to quantify the adaptation profile via the change in perceived sensation intensity over time. Approach: Noninvasive stimulation of the peripheral nerve bundles evoked haptic perception using a 2x5 electrode grid placed along the medial side of the upper arm near the median and ulnar nerves. An electrode pair that evoked haptic sensation along the forearm and hand was selected. During a trial of 110-s of continuous stimulation, a constant stimulus amplitude just below the motor threshold was delivered. Each subject was instructed to press on a force transducer producing a force amplitude matched with the perceived intensity of haptic sensation. Main Findings: A force decay (i.e., intensity of sensation) was observed in all 7 subjects. Variations in the rate of decay and the start of decay across subjects were also observed. Significance: The preliminary findings established the sensory adaptation profile of peripheral nerve stimulation. Accounting for these subject-specific profiles of adaptation can allow for more stable communication between a robotic device and a user. Additionally, sensory adaptation characterization can promote the development of new stimulation strategies that can mitigate these observed adaptations, allowing for a better and more stable human-machine interaction experience. 

A reliable and functional neural interface is necessary to control individual finger movements of assistive robotic hands. Non-invasive surface electromyogram (sEMG) can be used to predict fingertip forces and joint kinematics continuously. However, concurrent prediction of kinematic and dynamic variables in a continuous manner remains a challenge. The purpose of this study was to develop a neural decoding algorithm capable of concurrent prediction of fingertip forces and finger dynamic movements. High-density electromyogram (HD-EMG) signal was collected during finger flexion tasks using either the index or middle finger: isometric, dynamic, and combined tasks. Based on the data obtained from the two first tasks, motor unit (MU) firing activities associated with individual fingers and tasks were derived using a blind source separation method. MUs assigned to the same tasks and fingers were pooled together to form MU pools. Twenty MUs were then refined using EMG data of a combined trial. The refined MUs were applied to a testing dataset of the combined task, and were divided into five groups based on the similarity of firing patterns, and the populational discharge frequency was determined for each group. Using the summated firing frequencies obtained from five groups of MUs in a multivariate linear regression model, fingertip forces and joint angles were derived concurrently. The decoding performance was compared to the conventional EMG amplitude-based approach. In both joint angles and fingertip forces, MU-based approach outperformed the EMG amplitude approach with a smaller prediction error (Force: 5.36±0.47 vs 6.89±0.39 %MVC, Joint Angle: 5.0±0.27° vs 12.76±0.40°) and a higher correlation (Force: 0.87±0.05 vs 0.73±0.1, Joint Angle: 0.92±0.05 vs 0.45±0.05) between the predicted and recorded motor output. The outcomes provide a functional and accurate neural interface for continuous control of assistive robotic hands.