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1. Distribution of M-Wave and H-Reflex in Hand Muscles Evoked via Transcutaneous Nerve Stimulation: A Preliminary Report

   https://doi.org/10.1109/EMBC46164.2021.9630300  

   Vargas, Luis ; Baratta, John ; Hu, Xiaogang ( November 2021 , 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Neuromuscular electrical stimulation (NMES) targeting the muscle belly is commonly used to restore muscle strength in individuals with neurological disorders. However, early onset of muscle fatigue is a major limiting factor. Transcutaneous nerve stimulation (TNS) can delay muscle fatigue compared with traditional NMES techniques. However, the recruitment of Ia afferent fibers has not be specifically targeted to maximize muscle activation through the reflex pathway, which can lead to more orderly recruitment of motor units, further delaying fatigue. This preliminary study assessed the distribution of M-wave and H-reflex of intrinsic and extrinsic finger muscles. TNS was delivered using an electrode array placed along the medial side of the upper arm. Selective electrode pairs targeted the median and ulnar nerves innervating the finger flexors. High-density electromyography (HD EMG) was utilized to quantify the spatial distribution of the elicited activation of finger intrinsic and extrinsic muscles along the hand and forearm. The spatial patterns were characterized through isolation of the M-wave and H-reflex across various stimulation levels and EMG channels. Our preliminary results showed that, by altering the stimulation amplitude, distinct M-wave and H-reflex responses were evoked across EMG channels. In addition, distinct stimulation locations appeared to result in varied levels of reflexmore »recruitment. Our findings indicate that it is possible to adjust stimulation parameters to maximize reflex activation, which can potentially facilitate physiological recruitment order of motoneurons.« less
2. Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities

   https://doi.org/10.1109/TBME.2022.3232067  

   Roy, Rinku ; Zheng, Yang ; Kamper, Derek G. ; Hu, Xiaogang ( April 2023 , IEEE Transactions on Biomedical Engineering)

   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:more »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.« less
3. Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

   https://doi.org/10.1142/S0129065721500106  

   Zheng, Yang ; Hu, Xiaogang ( April 2021 , International Journal of Neural Systems)

   A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decodingmore »method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.« less
4. Abstracts From the 2018 Annual Meeting

   https://doi.org/10.1177/1545968318817151  

   Augenstein, T ; Washabaugh, E ; Remy, C ; Krishnan, C ( November 2018 , Neurorehabilitation and Neural Repair)

   Muscle weakness and loss of independent joint control are the 2 most common neuromotor impairments after stroke. While there are a number of approaches to improve poststroke muscle weakness, there are currently no rehabilitation strategies that directly target a patient’s inability to match and independently activate the normal patterned muscle coordination strategies, or “muscle synergies.” Our goal is to develop an EMG-based controller for retraining healthy muscle synergies in patients with stroke-related disabilities. The controller can be integrated into rehabilitation robots for their ability to structure the robot’s force output based on input EMG activity. However, developing such a controller would require a clear understanding of the relationship between the applied force from a rehabilitation robot and the resulting changes to a patient’s muscle synergies. Therefore, this study was performed to quantify how the muscle synergies of horizontal planar-reaching are affected by direction of an applied force at the end-effector (ie, hand). A 2 DOF, 10 muscle model was developed in MATLAB using parameters obtained from the OpenSim (version 3.3) open source software system. Simulation experiments were then performed in MATLAB to investigate the relationship between the applied force and the resulting muscle synergies. The simulated event was composed ofmore »several trials of the same righthanded, planar, multidirectional reaching task from 0° (to the right) to 360°. Each trial applied a different steering force direction at the subject’s hand, varying from −45° to 45° relative to the reaching direction. The simulation trials were also validated by evaluating the EMG patterns of a healthy subject when performing the same reaching task with varying steering force directions. For the 0° steering force trials, the muscle synergies and their activation timings were extracted using nonnegative matrix factorization (NMF). For all other trials, the synergy matrix was fixed and the activation timings were extracted from the product of the EMG of that trial and the pseudo-inverse of the synergy matrix from the 0° steering force trial. By fixing the synergy matrix in the trials with steering forces, we can directly track activation changes of a certain synergy as steering force is varied. For both simulation and experimental trials, circular statistics revealed a linear relationship between changes in steering force direction and principal direction of synergy activation. These results suggest that the activation of a synergy can be controlled directly by the direction of an applied steering force. This has relevant implications in synergy-based controller design because a computer can easily manipulate a patient’s muscle synergies and track the changes while avoiding the computational expense of NMF. In addition, similar analysis could be used to extract the relationship between applied forces and changes in synergies for other types of motion.« less
5. Asynchronous axonal firing patterns evoked via continuous subthreshold kilohertz stimulation

   https://doi.org/10.1088/1741-2552/acc20f  

   Vargas, Luis ; Musselman, Eric D. ; Grill, Warren M. ; Hu, Xiaogang ( March 2023 , Journal of Neural Engineering)

   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 alsomore »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.

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