Effect of initial joint position on nerve-cuff recordings of muscle afferents in rabbits

Flexible Peripheral Nerve Interfacing Electrode for Joint Position Control in Closed-Loop Neuromuscular Stimulation

Addressing peripheral nerve disorders with electronic medicine poses significant challenges, especially in replicating the dynamic mechanical properties of nerves and understanding their functionality. In the field of electronic medicine, it is crucial to design a system that thoroughly understands the functions of the nervous system and ensures a stable interface with nervous tissue, facilitating autonomous neural adaptation. Herein, we present a novel neural interface platform that modulates the peripheral nervous system using flexible nerve electrodes and advanced neuromodulation techniques. Specifically, we have developed a surface-based inverse recruitment model for effective joint position control via direct electrical nerve stimulation. Utilizing barycentric coordinates, this model constructs a three-dimensional framework that accurately interpolates inverse isometric recruitment values across various joint positions, thereby enhancing control stability during stimulation. Experimental results from rabbit ankle joint control trials demonstrate our model's effectiveness. In combination with a proportional-integral-derivative (PID) controller, it shows superior performance by achieving reduced settling time (less than 1.63 s), faster rising time (less than 0.39 s), and smaller steady-state error (less than 3 degrees) compared to the legacy model. Moreover, the model's compatibility with recent advances in flexible interfacing technologies and its integration into a closed-loop controlled functional neuromuscular stimulation (FNS) system highlight its potential for precise neuroprosthetic applications in joint position control. This approach marks a significant advancement in the management of neurological disorders with advanced neuroprosthetic solutions.

Peripheral neurostimulation for encoding artificial somatosensations

The direct neural stimulation of peripheral or central nervous systems has been shown as an effective tool to treat neurological conditions. The electrical activation of the nervous sensory pathway can be adopted to restore the artificial sense of touch and proprioception in people suffering from sensory-motor disorders. The modulation of the neural stimulation parameters has a direct effect on the electrically induced sensations, both when targeting the somatosensory cortex and the peripheral somatic nerves. The properties of the artificial sensations perceived, as their location, quality and intensity are strongly dependent on the direct modulation of pulse width, amplitude and frequency of the neural stimulation. Different sensory encoding schemes have been tested in patients showing distinct effects and outcomes according to their impact on the neural activation. Here, I reported the most adopted neural stimulation strategies to artificially encode somatosensation into the peripheral nervous system. The real-time implementation of these strategies in bionic devices is crucial to exploit the artificial sensory feedback in prosthetics. Thus, neural stimulation becomes a tool to directly communicate with the human nervous system. Given the importance of adding artificial sensory information to neuroprosthetic devices to improve their control and functionality, the choice of an optimal neural stimulation paradigm could increase the impact of prosthetic devices on the quality of life of people with sensorimotor disabilities.

Compact Optical Nerve Cuff Electrode for Simultaneous Neural Activity Monitoring and Optogenetic Stimulation of Peripheral Nerves

Optogenetic stimulation of the peripheral nervous system is a novel approach to motor control, somatosensory transduction, and pain processing. Various optical stimulation tools have been developed for optogenetic stimulation using optical fibers and light-emitting diodes positioned on the peripheral nerve. However, these tools require additional sensors to monitor the limb or muscle status. We present herein a novel optical nerve cuff electrode that uses a single cuff electrode to conduct to simultaneously monitor neural activity and optogenetic stimulation of the peripheral nerve. The proposed optical nerve cuff electrode is designed with a polydimethylsiloxane substrate, on which electrodes can be positioned to record neural activity. We confirm that the illumination intensity and the electrical properties of the optical nerve cuff electrode are suitable for optical stimulation with simultaneous neural activity monitoring in Thy1::ChR2 transgenic mice. With the proposed electrode, the limb status is monitored with continuous streaming signals during the optical stimulation of anesthetized and moving animals. In conclusion, this optical nerve cuff electrode provides a new optical modulation tool for peripheral nervous system studies.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

ESTIMATION OF ANKLE JOINT ANGLE FROM PERONEAL AND TIBIAL ELECTRONEUROGRAMS — A MUSCLE SPINDLE MODEL APPROACH

This study develops a method for estimating the angle of a passively stretched ankle joint from electroneurograms (ENGs) based on structural muscle spindle models of the tibial and peroneal nerves. Passive ramp-and-hold and alternating stretches of the ankle joint are performed on an anesthetized rabbit. Two cuff electrodes are employed to measure the ENGs of peroneal and tibial nerves simultaneously. From the two ENG signals and the joint angle trajectory, two intrafusal muscle fiber models are constructed and their inverse models are derived. The results of the two models are combined to generate the final angle estimate. An optimization method, called sequential quadratic programming, is employed to find the model parameters that minimize the squared errors between the ankle angles predicted by the model and the measured ankle angles. The performance of the proposed approach is compared with those of an adaptive neuro-fuzzy inference system and an artificial neural network model. The results reveal that the proposed model has the best performance in estimating the ankle joint angle in large-range movements and the smallest tracing error. The proposed method effectively estimates the passive ankle joint angle using the inverse physiological model of an intrafusal muscle fiber.

State-space decoding of primary afferent neuron firing rates

Kinematic state feedback is important for neuroprostheses to generate stable and adaptive movements of an extremity. State information, represented in the firing rates of populations of primary afferent (PA) neurons, can be recorded at the level of the dorsal root ganglia (DRG). Previous work in cats showed the feasibility of using DRG recordings to predict the kinematic state of the hind limb using reverse regression. Although accurate decoding results were attained, reverse regression does not make efficient use of the information embedded in the firing rates of the neural population. In this paper, we present decoding results based on state-space modeling, and show that it is a more principled and more efficient method for decoding the firing rates in an ensemble of PA neurons. In particular, we show that we can extract confounded information from neurons that respond to multiple kinematic parameters, and that including velocity components in the firing rate models significantly increases the accuracy of the decoded trajectory. We show that, on average, state-space decoding is twice as efficient as reverse regression for decoding joint and endpoint kinematics.

Microengineered neural probes for in vivo recording

One of the great challenges facing medicine is the repair of the damaged nervous system. Due to the limited capacity of the central (and to a lesser extent the peripheral) nervous systems to regenerate, damage such as spinal cord injury can often result in permanent paralysis. Researchers are attempting to overcome nerve injury by devising methods of sensing neural activity either in the brain or in the spinal cord or peripheral nervous system. This information can act as a control mechanism for either muscle stimulators (e.g. for restoring limb function) or providing function in some other way (such as controlling a cursor on a computer screen). Ideally, sensing devices are implanted into the body, directly accessing the nervous system. Whilst great advancements have been made in implantable neural stimulators, sensing of neural activity has proven to be a more difficult task. This chapter describes how microengineered probes allow construction of neuron-sized neural interfaces for enhanced recording in vivo.

Model-based ankle joint angle tracing by cuff electrode recordings of peroneal and tibial nerves

The main goal of the present study was to estimate the ankle joint angle from the peroneal and tibial electroneurography (ENG) recordings. Two single-channel cuff electrodes for recording ENG were placed on the proximal part of rabbit peroneal and tibial nerves respectively and static positioning and ramp-and-hold stretches were performed to characterize the static and dynamic ENG responses. An ENG model, consisting of static and dynamic parts, was constructed to relate ENG to ankle angle trajectory and an inverse ENG model was derived to predict ankle angle. The results showed that the new model could accurately estimate large-range ankle angles during and after ramp-and-hold movements. Our study provides a basis for implementing joint angle tracing without using artificial angle sensors.

Estimation of peroneal and tibial afferent activity from a multichannel cuff placed on the sciatic nerve

The afferent signals recorded with a multi-electrode cuff on the sciatic nerve were employed to investigate the possibility of extracting components ascending from the peroneal and tibial nerves. Two methods, an inverse regression model and principal component method, were studied. The parameters of inverse regression model, determined by data collected in semistatic conditions, were validated by data collected in dynamic conditions. The results showed that the regression model, which used only two channels of the sciatic recordings, was sufficient to separate the distal afferent components. The model, at the expense of requiring distal branch recordings for estimating model parameters, yielded better separation than the principal component method. In conclusion, peroneal and tibial afferent activity can be estimated from the sciatic nerve: the principal component method is suitable for applications focused on acquiring afferent information, whereas the inverse regression model is better for applications in which stimulations will be applied to the branches. The estimation technique provides a powerful tool for in vivo investigation of sensory information transmitted in a peripheral nerve and facilitates implementation of advanced functional neuromuscular stimulation systems.

On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.