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.

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Background

Several groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping). Algorithms have been developed to achieve this goal by processing electroneurographic (ENG) afferent signals recorded by using single-channel cuff electrodes. However, no efforts have been made so far to understand the number and type of detectable sensory events that can be differentiated from whole nerve recordings using this approach.

Methods

To this aim, ENG afferent signals, evoked by different sensory stimuli were recorded using single-channel cuff electrodes placed around the sciatic nerve of anesthetized rats. The ENG signals were digitally processed and several features were extracted and used as inputs for the classification. The work was performed on integral datasets, without eliminating any noisy parts, in order to be as close as possible to real application.

Results

The results obtained showed that single-channel cuff electrodes are able to provide information on two to three different afferent (proprioceptive, mechanical and nociceptive) stimuli, with reasonably good discrimination ability. The classification performances are affected by the SNR of the signal, which in turn is related to the diameter of the fibers encoding a particular type of neurophysiological stimulus.

Conclusions

Our findings indicate that signals of acceptable SNR and corresponding to different physiological modalities (e.g. mediated by different types of nerve fibers) may be distinguished.

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.

Biopotentials as command and feedback signals in functional electrical stimulation systems

Today Functional Electrical Stimulation (FES) is available as a clinical tool in muscle activation used for picking up objects, for standing and walking, for controlling bladder emptying, and for breathing. Despite substantial progress in development and new knowledge, many challenges remain to be resolved to provide a more efficient functionality of FES systems. The most important task of these challenges is to improve control of the activated muscles through open loop or feedback systems. Command and feedback signals can be extracted from biopotentials recorded from muscles (Electromyogram, EMG), nerves (Electroneurogram, ENG), and the brain (Electroencephalogram (EEG) or individual cells). This paper reviews work in which EMG, ENG, and EEG signals in humans have been used as command and feedback signals in systems using electrical stimulation of motor nerves to restore movements after an injury to the Central Nervous System (CNS). It is concluded that the technology is ready to push for more substantial clinical FES investigations in applying muscle and nerve signals. Brain-computer interface systems hold great prospects, but require further development of faster and clinically more acceptable technologies.

Improving signal reliability for on-line joint angle estimation from nerve cuff recordings of muscle afferents

Closed-loop functional electrical stimulation (FES) applications depend on sensory feedback, thus, it is important to continuously investigate new methods to obtain reliable feedback signals. The objective of the present paper was to examine the feasibility of using an artificial neural network (ANN) to predict joint angle from whole nerve cuff recordings of muscle afferent activity within a physiological range of motion. Furthermore, we estimated how small changes in joint angle that can be detected from the nerve cuff recordings. Neural networks were tested with data obtained from ten acute rabbit experiments in simulated, on-line experiments. The electroneurograms (ENG) of the tibial and peroneal nerves were recorded during passive ankle joint rotation. To decrease the joint angle prediction error with new rabbit data, we attempted to pretune the nerve signals and re-trained the ANNs with the pretuned data. With these procedures we were able to compensate for interrabbit variability. On average the mean prediction errors were less than 2.0 degrees (a total excursion of 20 degrees) and we were able to predict joint angles from muscle afferent activity with accuracy close to the best-estimated angular resolution. The angular resolution was found to depend on the initial joint angle and the actual step size taken and we found that there was a low probability of detecting joint angle changes less than 1.5 degrees. We thus suggest that muscle afferent activity is applicable as feedback in real-time closed-loop control, when the motion speed is restricted and when the movement is limited to a portion of the joint's physiological range.

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

The objective was to characterize nerve-cuff recordings of muscle afferents to joint rotation over a large part of the physiological joint range. This information is needed to develop control strategies for functional electrical stimulation (FES) systems using muscle afferent signals for sensory feedback. Five acute rabbit experiments were performed. Tripolar cuff electrodes were implanted around the tibial and peroneal divisions of the sciatic nerve in the rabbit's left leg. The electroneurograms (ENG) were recorded during passive ankle rotation, using a ramp-and-hold profile starting at seven different joint positions (excursion = 5 degrees, velocity = 10 degrees/s, initial positions 60 degrees, 70 degrees, 80 degrees, 90 degrees, 100%, 110 , and 120 ). The amplitude of the afferent activity was dependent on the initial joint position. The steady-state sensitivity of both nerve responses increased with increasing joint flexion, whereas the dynamic sensitivity increased initially but then decreased. The results indicate that recordings of the muscle afferents may provide reliable information over only a part of the physiological joint range. Despite this limitation, muscle afferent activity may be useful for motion feedback if the movement to be controlled is within a narrow joint range such as postural sway.

Effect of agonist-antagonist electrical stimulation on muscle afferent recordings in anesthetized rabbits

Objective. Sensory feedback extracted from muscle afferents is an approach to achieve closed-loop control of paralyzed muscles using functional electrical stimulation (FES). The objective of the present study was to characterize the effect of agonist-antagonist electrical stimulation on nerve cuff recordings of muscle afferent activity. Methods. Cuff electrodes were implanted around the tibial and peroneal nerve branches in five acute rabbit experiments. Two wires were implanted in each of the tibialis anterior (TA) and the lateral gastrocnemius (LG) muscles to obtain bipolar, intramuscular stimulation. Electroneurograms (ENG) were recorded during trapezoidal rotations of the ankle joint and compared during periods (25%, 50% and 100% of maximal force) with and without electrical stimulation of the muscle. Results. The activity from a stretched and electrically stimulated muscle showed the same pattern as the recordings from a matched nonstimulated muscle. The background afferent activity increased with increasing level of muscle stimulation. The static and dynamic sensitivities were not found to be different, except in one case (peroneal nerve at 100% TA recruitment). Discussion. The main contribution to the tibial activity was believed to originate from muscle afferents in nonstimulated, synergist muscles. The main contribution to the peroneal activity was believed to be from muscle afferents within the muscle being stimulated. It was suggested that the increased background activity could be attributed to the increased activity of the Golgi tendon organs. Conclusions. Sensory information about joint flexion and joint extension are preserved in muscle afferent recordings from electrically activated muscles at low and intermediate stimulation levels, but it still has to be shown whether muscle afferent information can be useful as sensory feedback in FES control.