Instrumentation for ENG and EMG recordings in FES systems

An electronic circuit for analog processing of neural (electroneurogram or ENG) and muscular (electromyogram or EMG) signals in functional electrical stimulation (FES) systems is described. The basic circuit consists of a low-noise gated preamplifier, bandpass filter, amplifier, and a blanking circuit to minimize stimulation artifacts during electrical stimulation. This device was tested in chronic recordings using a triphasic cliff electrode for nerves and epimysial electrodes for muscles in the hind limbs of cats. The device was used for nerve recordings in the presence of electrical stimulation of muscles in the same leg. The recordings showed rejection of stimulation and muscle (M-wave) artifacts, while retaining the information of interest     

Neuro-fuzzy extraction of angular information from muscle afferentsfor ankle control during standing in paraplegic subjects: an animalmodel

This paper is part of a project whose aim is the implementation of closed-loop control of ankle angular position during functional electrical stimulation (FES) assisted standing in paraplegic subjects using natural sensory information. In this paper, a neural fuzzy (NF) model is implemented to extract angular position information from the electroneurographic signals recorded from muscle afferents using cuff electrodes in an animal model. The NF model, named dynamic nonsingleton fuzzy logic system is a Mamdani-like fuzzy system, implemented in the framework of recurrent neural networks. The fuzzification procedure implemented was the nonsingleton technique which has been shown in previous works to be able to take into account the uncertainty in the data. The proposed algorithm was tested in different situations and was able to predict reasonably well the ankle angular trajectories especially for small excursions (as during standing) and when the stimulation sites are far from the registration sites. This suggests it may be possible to use activity from muscle afferents recorded with cuff electrodes for FES closed-loop control of ankle position during quite standing.     

A new means of transcutaneous coupling for neural prostheses

Neural prostheses are electronic stimulators that activate nerves to restore sensory or motor functions. Implanted neural prostheses receive command signals and in some cases energy to recharge their batteries through the skin by telemetry. Here, we describe a new approach that eliminates the implanted stimulator. Stimulus pulse trains are passed between two surface electrodes placed on the skin. An insulated lead with conductive terminals at each end is implanted inside the body. One terminal is located under the cathodal surface electrode and the other is attached to a nerve targeted for stimulation. A fraction (10%-15%) of the current flowing between the surface electrodes is routed through the implanted lead. The nerve is stimulated when the amount of routed current is sufficient. The aims of this study were to establish some basic electrical properties of the system and test long-term stability in chronic implants. Stimulation of the nerve innervating the ankle flexors produced graded force over the full physiological range at amplitudes below threshold for evoking muscle contractions under the surface electrodes. Implants remained stable for over 8 mo. The findings provide the basis for a new family of neural prostheses.     

Gait phase information provided by sensory nerve activity duringwalking: applicability as state controller feedback for FES

In this study, we extracted gait-phase information from natural sensory nerve signals of primarily cutaneous origin recorded in the forelimbs of cats during walking on a motorized treadmill. Nerve signals were recorded in seven cats using nerve cuff or patch electrodes chronically implanted on the median, ulnar, and/or radial nerves. Features in the electroneurograms that were related to paw contact and lift-off were extracted by threshold detection. For four cats, a state controller model used information from two nerves (either median and radial, or ulnar and radial) to predict the timing of palmaris longus activity during walking. When fixed thresholds were used across a variety of walking conditions, the model predicted the timing of EMG activity with a high degree of accuracy (average error = 7.8%, standard deviation = 3.0%, n = 14). When thresholds were optimized for each condition, predictions were further improved (average error = 5.5%, standard deviation = 2.3%, n = 14). The overall accuracy with which EMG timing information could be predicted using signals from two cutaneous nerves for two constant walking speeds and three treadmill inclinations for four cats suggests that natural sensory signals may be implemented as a reliable source of feedback for closed-loop control of functional electrical stimulation (FES).     

Characterization of signals and noise rejection with bipolarlongitudinal intrafascicular electrodes

Longitudinal intrafascicular electrodes (LIFEs) are fine electrodes threaded into the extracellular space between axons in peripheral nerves or spinal roots. The authors are developing these electrodes for application in functional electrical stimulation and in basic physiology. An area of concern in chronic recording application of LIFEs is the possibility of electromyogram and other external noise sources masking the recorded neural signals. The authors characterized neural signals recorded by LIFEs and confirmed by three independent methods that increasing interelectrode spacing for bipolar LIFEs increases signal amplitude. The spectrum of neural signal from bipolar and monopolar LIFE lies between 300 Hz and 10 kHz. The amplitude of the spectrum increases with increasing interelectrode spacing, although the distribution is not affected. Single unit analysis of LIFE recordings show that they record selectively from units closest to the electrode active site. Units with conduction velocities ranging from 50-120 m/s were identified. Extraneural noise, as stimulus artifact or electromyogram, is much reduced with bipolar LIFE recording, as compared to monopolar recordings. Relative improvement in neural signal to extraneural noise increases with interelectrode spacing up to about 2 mm. Since there is no further improvement beyond 2 mm, the authors conclude that the preferred interelectrode spacing for bipolar LIFEs is 2 mm     

Artificial neural network control of FES in paraplegics for patientresponsive ambulation

Describes a binary adaptive resonance theory (ART-1)-based artificial neural network (ANN) adapted for controlling functional electrical stimulation (FES) to facilitate patient-responsive ambulation by paralyzed patients with spinal cord injures. This network is to serve as a controller in an FES system developed by the first author which is presently in use by 300 patients worldwide (still without ANN control) and which was the first and the only FES system approved by the FDA. The proposed neural network discriminates above-lesion upper-trunk electromyographic (EMG) time series to activate standing and walking functions under FES and controls FES stimuli levels using response-EMG signals. For this particular application, the authors introduce several modifications of the ART-1 for pattern recognition and classification. First, a modified on-line learning rule is proposed. The new rule assures bidirectorial modification of the stored patterns and prevents noise interference. Second, a new reset rule is proposed which prevents “exact matching” when the input is a subset of the chosen pattern. The authors show the applicability of a single ART-1-based structure to solving two problems, namely, 1) signal pattern recognition and classification, and 2) control. This also facilitates ambulation of paraplegics under FES, with adequate patient interaction in initial system training, retraining the network when needed, and in allowing patient's manual override in the ease of error, where any manual override serves as a retraining input to the neural network. Thus, the practical control problems (arising in actual independent patient ambulation via FES) were all satisfied by a relatively simple ANN design     

Closed-loop control of ankle position using muscle afferentfeedback with functional neuromuscular stimulation

Describes a closed-loop functional neuromuscular stimulation system that uses afferent neural activity from muscle spindle fibers as feedback for controlling position of the ankle joint. Ankle extension against a load was effected by neural stimulation through a dual channel intrafascicular electrode of a fascicle of the tibial nerve that innervated the gastrocnemius muscle. Ankle joint angle was estimated from recordings of tibialis anterior and lateral gastrocnemius spindle fiber activity made with dual channel intrafascicular electrodes. Experiments were conducted in neurally intact anesthetized cats and in unanesthetized decerebrate cats to demonstrate the feasibility of this system. The system was able to reach and maintain a fixed target ankle position in the presence of a varying external moment ranging in magnitude between 7.3 and 22 N-cm opposing the action of the ankle extensor, as well as track a sinusoidal target ankle position up to a frequency of 1 Hz in the presence of a constant magnitude 22- or 37-N-cm external moment     

Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves

According to the feature of neural signals,a micro-electronic neural bridge(MENB)has been designed. It consists of two electrode arrays for neural signal detection and functional electrical stimulation(FES),and a microelectronic circuit for signal amplifying,processing,and FES driving.The core of the system is realized in 0.5-μm CMOS technology and used in animal experiments.A special experimental strategy has been designed to demonstrate the feasibility of the system.With the help of the MENB,the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad.According to the coherence analysis between the source and regenerated neural signals,the controlled spinal toad’s sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73.This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated.The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies.     

A Chip for an Implantable Neural Stimulator

This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation (FES). The purpose of FES is to restore muscular control in disabled patients. The chip performs all the signal processing required in an implanted neural stimulator. The power and digital data transmission to the stimulator passes through a 5 MHz inductive link. From the signals transmitted to the stimulator, the chip is able to generate charge-balanced current pulses with a controllable length up to 256 s and an amplitude up to 2 mA, for stimulation of nerve fibers. The quiescent current consumption of the chip is approx. 650 A at supply voltages of 6–12 V, and its size is 3.9×3.5 mm². It has 4 output channels for use in a multipolar cuff electrode.     

Toward functional magnetic stimulation (FMS) theory and experiment

Examines the use of magnetic fields to functionally stimulate peripheral nerves. All electric fields are induced via a changing magnetic field whose flux is entirely confined within a closed magnetic circuit. Induced electric fields are simulated using a nonlinear boundary element solver. The induced fields are solved using duality theory. The accuracy of these predictions is verified by saline bath experiments. Next, the theory is applied to the stimulation of nerves using small, partially occluded ferrite and laminated vanadium permendur cores. Experiments demonstrate the successful stimulation of peripheral nerves in the African bullfrog with 11 mA, 153 mV excitations. These results offer a new vista of possibilities in the area of functional nerve stimulation. Unlike functional electric stimulation (FES), FMS does not involve any half cell reactions, and thus would not have the commensurate FES restrictions regarding balanced biphasic stimulation, strength duration balances, and oxidation issues, always exercising care that the electrodes remain in the reversible operating regime     

A multichannel FES system for the restoration of motor functions inhigh spinal cord injury patients: a respiration-controlled system formultijoint upper extremity

A multichannel functional electrical stimulation (FES) system for the restoration of quadriplegic upper extremity function is described. The system is composed of a personal computer NEC PC-8801mkII, peripheral electronic circuits, CRT display and respiratory sensors for volitional control by the patient, and percutaneous electrodes. A C4 quadriplegic patient could drink canned tea by herself by using this FES system. Distinct features of the system are as follows: 1) Versatile volitional control was realized by controlling the memory allocation of the stored stimulation data by voluntary respiratory signals. 2) Sophisticated fine control of the fingers, wrists, and elbow was realized by creating the multichannel stimulation data from recorded myoelectric activities of normal subjects during movements of the upper limb.     

Setting adaptive spike detection threshold for smoothed TEO based on robust statistics theory

We propose a novel approach aimed at adaptively setting the threshold of the smoothed Teager energy operator (STEO) detector to be used in extracellular recording of neural signals. In this proposed approach, to set the adaptive threshold of the STEO detector, we derive the relationship between the low-order statistics of its input signal and the ones of its output signal. This relationship is determined with only the background noise component assumed to be present at the input. Robust statistics theory techniques were used to achieve an unbiased estimation of these low-order statistics of the background noise component directly from the neural input signal. In this paper, the emphasis is made on extracellular neural recordings. However, the proposed method can be used in the analysis of different biomedical signals where spikes are important for diagnostic (e.g., ECG, EEG, etc.). We validated the efficacy of the proposed method using synthetic neural signals constructed from real neural recordings signals. Four different sets of extracellular recordings from four distinct neural sources have been exploited to that purpose. The first dataset is recorded from an adult male monkey using the Utath 10×10 microelectrode array implemented in the prefrontal cortex, the second one was obtained from the visual cortex of a rat using a stainless-steel-tipped microelectrode, the third dataset came from recording in a human medial lobe using intracranial electrode, and finally, the fourth one was extracted from recordings in a macaque parietal cortex using a single tetrode. Simulation results show that our approach is effective and robust, and outperforms state-of-the-art adaptive detection methods in its category (i.e., efficient and simple, and do not require a priori knowledge about neural spike waveforms shapes).     

A Neuro-Sliding-Mode Control With Adaptive Modeling of Uncertainty for Control of Movement in Paralyzed Limbs Using Functional Electrical Stimulation

During the past several years, several strategies have been proposed for control of joint movement in paraplegic subjects using functional electrical stimulation (FES), but developing a control strategy that provides satisfactory tracking performance, to be robust against time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, muscle fatigue, and external disturbances, and to be easy to apply without any re-identification of plant dynamics during different experiment sessions is still an open problem. In this paper, we propose a novel control methodology that is based on synergistic combination of neural networks with sliding-mode control (SMC) for controlling FES. The main advantage of SMC derives from the property of robustness to system uncertainties and external disturbances. However, the main drawback of the standard sliding modes is mostly related to the so-called chattering caused by the high-frequency control switching. To eliminate the chattering, we couple two neural networks with online learning without any offline training into the SMC. A recurrent neural network is used to model the uncertainties and provide an auxiliary equivalent control to keep the uncertainties to low values, and consequently, to use an SMC with lower switching gain. The second neural network consists of a single neuron and is used as an auxiliary controller. The control law will be switched from the SMC to neural control, when the state trajectory of system enters in some boundary layer around the sliding surface. Extensive simulations and experiments on healthy and paraplegic subjects are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed neuroadaptive SMC. The results show that the neuro-SMC provides accurate tracking control with fast convergence for different reference trajectories and could generate control signals to compensate the muscle fatigue and reject the external disturbance.     

Detection and classification of sensory information from acute spinal cord recordings

One avenue of research for partial restoration of function following spinal cord injury is the use of neural prostheses, an example of which is functional electrical stimulation (FES) devices for motor functions. Neural prostheses may also be useful for the extraction of sensory information directly from the nervous system. We suggest the spinal cord as a possible site for the detection of peripheral sensory information from neural activity alone. Acute multichannel extracellular recordings were used to extract neural spike activity elicited from peripheral sensations from the spinal cords of rats. To test the recording method and classification potential, eight classes of sensory events were recorded consisting of electrical stimulation of seven locations on rat forepaws, and another class of data during which no stimulus was present. A dual-stage classification scheme using principal component analysis and k-Means clustering was devised to classify the sensory events during single trials. The eight tasks were correctly identified at a mean accuracy of 96%. Thus, we have shown the methodology to detect and classify peripheral sensory information from multichannel recordings of the spinal cord. These methods may be useful, for example, in a closed-loop FES for restoration of hand grasp.     

Machine learning in control of functional electrical stimulationsystems for locomotion

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) and the inductive learning algorithm (IL). Results to date suggest that, given the same training data, the IL learned faster than the ALN while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified     

Prediction of Distal Arm Posture in 3-D Space From Shoulder Movements for Control of Upper Limb Prostheses

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation (FES) system or a transhumeral prosthesis. Such prostheses require, at the most basic level, the control of endpoint position in three dimensions, hand orientation, and grasp. Spatiotemporal synergies exist between the proximal and distal arm joints for goal-oriented reaching movements as performed by able-bodied subjects. To fit these synergies, we utilized three-layer artificial neural networks. These networks could be used as a means for obtaining user intent information during reaching movements. We conducted reaching experiments in which subjects reached to and grasped a handle in a three-dimensional gantry. In our previous work, the three rotational angles at the shoulder were used to predict elbow flexion/extension angle during reaches on a two-dimensional plane. In this paper, we extend this model to include the two translational movements at the shoulder as inputs and an additional output of forearm pronation/supination. Counterintuitively, as the complexity of the task and the complexity of the neural network architecture increased, the performance also improved.     

SRAM bitmap shape recognition and sorting using neural networks

This paper details the use of neural network technologies in the characterization of bit fail patterns occurring on SRAM chips as an alternative to the more traditional rule-based or knowledge-based approach to fail-pattern occurrence and classification analysis. The results of bit fail pattern count analyses are used both for fault analysis post-processing and manufacturing yield improvement methodologies. The move toward neural network implementation comes in response to prohibitively long processing times required for implementation of rule-based algorithms on more complex devices and the added flexibility of a neural network to learn new fail types in a more adaptive mode. An unsupervised approach to fail pattern identification was implemented on a 128 K SRAM chip using a two-layer Kohonen Self Organizing Map for identification and concurrence of bit fail pattern categories within SRAM chips. A second network utilized a multilayer perceptron (MLP) architecture with backpropagation of error for prediction of the number of occurrences per bitmap of each of the 34 previously identified shape types. The MLP used the output of a SOM as its input vector to assist in the feature extraction by shape type. Both trained networks out-performed existing rule-based algorithms both in ability to identify bit fail pattern types, frequency counts, and speed of processing     

低功耗CMOS神经束功能电激励信号产生电路

采用华润上华微电子公司0.6μm CMOS工艺设计了低功耗神经功能电激励集成电路.该电路适用于以卡肤电极作为激励电极的可植入式神经信号桥接系统,可以用来激励脊椎动物的脊髓神经或其他神经束.电路包括输入级差分预放大电路、增益级放大电路和输出电路.为满足体内植入式神经功能电激励的要求,该集成电路避免使用任何片外元件,实现了单片集成.根据神经信号的特点,神经功能激励电路的频率响应带宽设计为1Hz～400kHz,输出电阻为90Ω时的增益为66dB,可以在3～5V的工作电压下正常工作.采用满摆幅输出级提高了有效的激励电压输出.测试结果表明,电路的带宽和增益符合设计要求,直流功耗低于6mW,达到了设计目标.     

A Reliable Myoelectric Signal Detector Based on the Propagation Characteristics of Motor Unit Action Potentials

A method was proposed for detecting and rejecting motion artifacts superimposed on myoelectric (ME) signals which are used in the estimation of muscular activity, in the control of powered prostheses, and in other applications. The method is based on the propagation characteristics of motor unit action potentials derived with multiple surface electrodes placed along the muscle fibers. The contamination of artifacts was detected by the decrease of the normalized correlation coefficient calculated at the time shift corresponding to the potential propagation. The product of two correlated signals was found to be less affected by the artifacts and was a better estimate of muscular activity than the root mean square of the ME signal which is conventionally used in the applications of ME signals.