Noise and selectivity of velocity-selective multi-electrode nerve cuffs

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography. Therefore, this study compares the two algorithms using in-vivo recordings of electrically evoked compound action potentials from the ulnar nerve of a pig. The performance of these algorithms was assessed using the velocity quality factor (Q-factor), computational time and the influence of the number of channels. The results show that the performance of both algorithms is significantly influenced by the number of channels in the recording array, with accuracies ranging from 77% with only two channels to 98% for 11 channels. Both algorithms were comparable in accuracy and Q-factor for all channels, with the delay-and-add having a slight advantage in the Q-factor.

The Design of a Low Noise, Multi-Channel Recording System for Use in Implanted Peripheral Nerve Interfaces

In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits. This paper introduces formal analyses of the performance of two commonly used filter networks. To achieve a manageable set of design equations, the state equations of the complete system are simplified. The derived equations help the designer in the task of creating an interface network for specific applications. The noise, crosstalk and common-mode rejection ratio (CMRR) of the recording system are computed as a function of electrode impedance, filter component values and amplifier specifications. The effect of electrode mismatches as an inherent part of any multi-electrode system is also discussed, using measured data taken from a MEC implanted in a sheep. The accuracy of these analyses is then verified by simulations of the complete system. The results indicate good agreement between analytic equations and simulations. This work highlights the critical importance of understanding the effect of interface circuits on the performance of neural recording systems.

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Decoding information from the peripheral nervous system via implantable neural interfaces remains a significant challenge, considerably limiting the advancement of neuromodulation and neuroprosthetic devices. The velocity selective recording (VSR) technique has been proposed to improve the classification of neural traffic by combining temporal and spatial information through a multi-electrode cuff (MEC). Therefore, this study investigates the feasibility of using the VSR technique to characterise fibre type based on the electrically evoked compound action potentials (eCAP) propagating along the ulnar nerve of pigs in vivo. A range of electrical stimulation parameters (amplitudes of 50 μA-10 mA and pulse durations of 100 μs, 500 μs, 1000 μs, and 5000 μs) was applied on a cutaneous and a motor branch of the ulnar nerve in nine Danish landrace pigs. Recordings were made with a 14 ring MEC and a delay-and-add algorithm was used to convert the eCAPs into the velocity domain. The results revealed two fibre populations propagating along the cutaneous branch of the ulnar nerve, with mean velocities of 55 m/s and 21 m/s, while only one dominant fibre population was found for the motor branch, with a mean velocity of 63 m/s. Because of its simplicity to provide information on the fibre selectivity and direction of propagation of nerve fibres, VSR can be implemented to advance the performance of the bidirectional control of neural prostheses and bioelectronic medicine applications.

Assessment of the Use of Multi-Channel Organic Electrodes to Record ENG on Small Nerves: Application to Phrenic Nerve Burst Detection

Effective closed-loop neuromodulation relies on the acquisition of appropriate physiological control variables and the delivery of an appropriate stimulation signal. In particular, electroneurogram (ENG) data acquired from a set of electrodes applied at the surface of the nerve may be used as a potential control variable in this field. Improved electrode technologies and data processing methods are clearly needed in this context. In this work, we evaluated a new electrode technology based on multichannel organic electrodes (OE) and applied a signal processing chain in order to detect respiratory-related bursts from the phrenic nerve. Phrenic ENG (pENG) were acquired from nine Long Evans rats in situ preparations. For each preparation, a 16-channel OE was applied around the phrenic nerve’s surface and a suction electrode was applied to the cut end of the same nerve. The former electrode provided input multivariate pENG signals while the latter electrode provided the gold standard for data analysis. Correlations between OE signals and that from the gold standard were estimated. Signal to noise ratio (SNR) and ROC curves were built to quantify phrenic bursts detection performance. Correlation score showed the ability of the OE to record high-quality pENG. Our methods allowed good phrenic bursts detection. However, we failed to demonstrate a spatial selectivity from the multiple pENG recorded with our OE matrix. Altogether, our results suggest that highly flexible and biocompatible multi-channel electrode may represent an interesting alternative to metallic cuff electrodes to perform nerve bursts detection and/or closed-loop neuromodulation.

The Effects of the Presence of Multiple Conduction Velocities in the Analysis of Electrically-Evoked Compound Action Potentials (eCAPs)

New methods for the analysis of electrically-evoked compound action potentials (eCAPs) are described. Mammalian nerves tend to have broad multi-modal distributions of fibre diameters, which translates into a spread of conduction velocities. The method of velocity selective recording (VSR) is unable to distinguish between this spectral spread and the transfer function of the system. The concept of the velocity impulse function (VIF) is introduced as a tool to differentiate between these signal and system attributes. The new methods enable separate estimates of velocity spectral broadening and signal-to-noise ratio (SNR) to be obtained.

Selective Recording of Urinary Bladder Fullness from the Extradural Sacral Roots. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:3873-3876. [PMID: 33018846 DOI: 10.1109/embc44109.2020.9176038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Managing the urinary bladder is of primary importance to clinicians and patients after trauma to the spinal cord. Sacral Anterior Root Stimulators that control the bladder have been available as clinical technology for many years, however these devices cannot measure the fullness of the urinary bladder or detect the onset of reflex voiding. In order to address this fundamental limitation, it is necessary to develop a method for recording the neural signals that encode bladder fullness. This paper presents a proof of concept technique for recording bladder afferents from the extradural sacral roots using a multiple electrode cuff. Results are provided from acute in-vivo experiments performed in sheep.

A review for the peripheral nerve interface designer

Informational density and relative accessibility of the peripheral nervous system make it an attractive site for therapeutic intervention. Electrode-based electrophysiological interfaces with peripheral nerves have been under development since the 1960s and, for several applications, have seen widespread clinical implementation. However, many applications require a combination of neural target resolution and stability which has thus far eluded existing peripheral nerve interfaces (PNIs). With the goal of aiding PNI designers in development of devices that meet the demands of next-generation applications, this review seeks to collect and present practical considerations and best practices which emerge from the literature, including both lessons learned during early PNI development and recent ideas. Fundamental and practical principles guiding PNI design are reviewed, followed by an updated and critical account of existing PNI designs and strategies. Finally, a brief survey of in vitro and in vivo PNI characterization methods is presented.

A microfabricated nerve-on-a-chip platform for rapid assessment of neural conduction in explanted peripheral nerve fibers

Peripheral nerves are anisotropic and heterogeneous neural tissues. Their complex physiology restricts realistic in vitro models, and high resolution and selective probing of axonal activity. Here, we present a nerve-on-a-chip platform that enables rapid extracellular recording and axonal tracking of action potentials collected from tens of myelinated fibers. The platform consists of microfabricated stimulation and recording microchannel electrode arrays. First, we identify conduction velocities of action potentials traveling through the microchannel and propose a robust data-sorting algorithm using velocity selective recording. We optimize channel geometry and electrode spacing to enhance the algorithm reliability. Second, we demonstrate selective heat-induced neuro-inhibition of peripheral nerve activity upon local illumination of a conjugated polymer (P3HT) blended with a fullerene derivative (PCBM) coated on the floor of the microchannel. We demonstrate the nerve-on-a-chip platform is a versatile tool to optimize the design of implantable peripheral nerve interfaces and test selective neuromodulation techniques ex vivo.

Peripheral nerves have a complex physiology and it is therefore difficult to measure axonal activity in vitro. Here the authors make a nerve-on-a-chip platform to align peripheral nerves and permit measurement of conduction amplitude and velocity along several axons in a single experiment.

Velocity Selective Recording: A Demonstration of Effectiveness on the Vagus Nerve in Pig

Neural interfaces that can both stimulate and record from the peripheral nervous system are an important component of future bioelectronic devices. However, despite a long history of neurostimulation, there has been relatively little success in the design of a chronically implantable device for recording from peripheral nerves. This fundamental road block must be overcome if the design of advanced implantable devices is to continue. In this paper, we demonstrate the effectiveness of one method: velocity selective recording, a method that has been proposed as a tool for online neural recording that does not require training. We present results and analysis from invivo recordings made on the right vagus nerve of pig using a multiple-electrode cuff as a chronically implantable recording array.

First demonstration of velocity selective recording from the pig vagus using a nerve cuff shows respiration afferents

Neural interfaces have great potential to treat disease and disability by modulating the electrical signals within the nervous system. However, whilst neural stimulation is a well-established technique, current neural interfaces are limited by poor recording ability. Low signal amplitudes necessitate the use of highly invasive techniques that divide or penetrate the nerve, and as such are unsuitable for chronic implantation. In this paper, we present the first application of the velocity selective recording technique to the detection of respiration activity in the vagus nerve, which is involved with treatments for epilepsy, depression, and rheumatoid arthritis. Further, we show this using a chronically implantable interface that does not divide the nerve. We also validate our recording setup using electrical stimulation and we present an analysis of the recorded signal amplitudes. The recording interface was formed from a cuff containing ten electrodes implanted around the intact right vagus nerve of a Danish Landrace pig. Nine differential amplifiers were connected to adjacent electrodes, and the resulting signals were processed to discriminate neural activity based on conduction velocity. Despite the average single channel signal-to-noise ratio of - 5.8 dB, it was possible to observe distinct action potentials travelling in both directions along the nerve. Further, contrary to expectation given the low signal-to-noise ratio, we have shown that it was possible to identify afferent neural activity that encoded respiration. The significance of this is the demonstration of a chronically implantable method for neural recording, a result that will transform the capabilities of future neuroprostheses.

A New Method for Neural Spike Alignment: The Centroid Filter

Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process. We present a new centroid-based method and demonstrate its effectiveness using deterministic models of nerve signals. We show that spike alignment in the presence of noise is possible with a 30 dB reduction in minimum SNR compared with the conventional methods. We present a mathematical analysis of the centroid method, characterizing its fundamental operation and performance. Furthermore, we show that the centroid method lends itself particularly well to hardware realization, and we present results from a low-power implementation that operates on an FPGA, consuming ten times less power than conventional techniques - an important property for implanted devices. Our centroid method enables the accurate alignment of spikes in sub-0 dB SNR recordings and has the potential to enable the analysis of spikes in a wider range of environments than has been previously possible. Our method thus has the potential to influence significantly the design of electrophysiological recording systems in the future.

Directionally-sensitive peripheral nerve recording: bipolar nerve cuff design

Although bipolar nerve cuff electrodes (NCEs) are used in clinical neurostimulation therapies (e.g., epilepsy, obstructive sleep apnea), the use of this electrode as part of a closed-up controlled system is very limited. The literature points to high noise content (i.e., poor signal-to-noise ratio, SNR) as the primary reason for not using bipolar NCEs to record neural activity. In fact, over the past several decades, the pseudo-tripolar NCEs, the symmetric configuration of this electrode array (i.e., electrically-shorted side contacts) makes it very difficult to obtain directional information from the recorded neural activity (efferent vs. afferent). In this study, we investigated the feasibility of using a bipolar NCE to achieve low-noise, directionally-sensitive recording of peripheral nerve activity.

Ultra-low noise miniaturized neural amplifier with hardware averaging

OBJECTIVE

Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (<3 μVrms 700 Hz-7 kHz), thereby requiring a baseline noise of less than 1 μVrms for a useful signal-to-noise ratio (SNR). Flat interface nerve electrode (FINE) contacts alone generate thermal noise of at least 0.5 μVrms therefore the amplifier should add as little noise as possible. Since mainstream neural amplifiers have a baseline noise of 2 μVrms or higher, novel designs are required.

APPROACH

Here we apply the concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments.

MAIN RESULTS

We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating <1.5 μVrms total recording baseline noise when connected to a FINE placed on the sciatic nerve of an awake animal. An algorithm was introduced to find the value of N that can minimize both the power consumption and the noise in order to design a miniaturized ultralow-noise neural amplifier.

SIGNIFICANCE

These results demonstrate the efficacy of hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.

Improved Signal Processing Methods for Velocity Selective Neural Recording Using Multi-Electrode Cuffs

This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods (`delay-and-add') with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.

A device for emulating cuff recordings of action potentials propagating along peripheral nerves

This paper describes a device that emulates propagation of action potentials along a peripheral nerve, suitable for reproducible testing of bio-potential recording systems using nerve cuff electrodes. The system is a microcontroller-based stand-alone instrument which uses established nerve and electrode models to represent neural activity of real nerves recorded with a nerve cuff interface, taking into consideration electrode impedance, voltages picked up by the electrodes, and action potential propagation characteristics. The system emulates different scenarios including compound action potentials with selectable propagation velocities and naturally occurring nerve traffic from different velocity fiber populations. Measured results from a prototype implementation are reported and compared with in vitro recordings from Xenopus Laevis frog sciatic nerve, demonstrating that the electrophysiological setting is represented to a satisfactory degree, useful for the development, optimization and characterization of future recording systems.

Fibre-selective discrimination of physiological ENG using velocity selective recording: report on pilot rat experiments

This paper presents results from a pilot experiment in which the technique of velocity selective recording (VSR) was used to identify naturally occurring electroneurogram (ENG) signals within the intact nerve of a rat. Signals were acquired using a set of electrodes placed along the length of the nerve, formed from simple wire hooks. This basic form of recording has already been applied in-vivo to the analysis of electrically excited compound action potentials (CAPs) in both pig and frog, however, this method has never before been used to identify naturally occurring neural signals. Results in this paper highlight challenges which must be overcome in order for the transition to be made from electrically evoked potentials to naturally occurring signals.

A switched-capacitor front-end for velocity-selective ENG recording

Multi-electrode cuffs (MECs) have been proposed as a means for extracting additional information about the velocity and direction of nerve signals from multi-electrode recordings. This paper discusses certain aspects of the implementation of a system for velocity selective recording (VSR) where multiple neural signals are matched and summed to identify excited axon populations in terms of velocity. The approach outlined in the paper involves the replacement of the digital signal processing stages of a standard delay-matched VSR system with analogue switched-capacitor (SC) delay lines which promises significant savings in both size and power consumption. The system specifications are derived and two circuits, each composed of low-noise preamplifiers connecting to a 2nd rank SC gain stage, are evaluated. One of the systems provides a single-ended SC stage whereas the other system is fully differential. Both approaches are shown to provide the low-noise, low-power operation, practically identical channel gains and sample delay range required for VSR. Measured results obtained from chips fabricated in 0.8 μ m CMOS technology are reported.

Effect on signal-to-noise ratio of splitting the continuous contacts of cuff electrodes into smaller recording areas

Background

Cuff electrodes have been widely used chronically in different clinical applications. This neural interface has been dominantly used for nerve stimulation while interfering noise is the major issue when employed for recording purposes. Advancements have been made in rejecting extra-neural interference by using continuous ring contacts in tripolar topologies. Ring contacts provide an average of the neural activity, and thus reduce the information retrieved. Splitting these contacts into smaller recording areas could potentially increase the information content. In this study, we investigate the impact of such discretization on the Signal-to-Noise Ratio (SNR). The effect of contacts positioning and an additional short circuited pair of electrodes were also addressed.

Methods

Different recording configurations using ring, dot, and a mixed of both contacts were studied in vitro in a frog model. An interfering signal was induced in the medium to simulate myoelectric noise. The experimental setup was design in such a way that the only difference between recordings was the configuration used. The inter-session experimental differences were taken care of by a common configuration that allowed normalization between electrode designs.

Results

It was found that splitting all contacts into small recording areas had negative effects on noise rejection. However, if this is only applied to the central contact creating a mixed tripole configuration, a considerable and statistically significant improvement was observed. Moreover, the signal to noise ratio was equal or larger than what can be achieved with the best known configuration, namely the short circuited tripole. This suggests that for recording purposes, any tripole topology would benefit from splitting the central contact into one or more discrete contacts.

Conclusions

Our results showed that a mixed tripole configuration performs better than the configuration including only ring contacts. Therefore, splitting the central ring contact of a cuff electrode into a number of dot contacts not only provides additional information but also an improved SNR. In addition, the effect of an additional pair of short circuited electrodes and the “end effect” observed with the presented method are in line with previous findings by other authors.

Double-differential recording and AGC using microcontrolled variable gain ASIC

Low-power wearable recording of biopotentials requires acquisition front-ends with high common-mode rejection for interference suppression and adjustable gain to provide an optimum signal range to a cascading analogue-to-digital stage. A microcontroller operated double-differential (DD) recording setup and automatic gain control circuit (AGC) are discussed which reject common-mode interference and provide tunable gain, thus compensating for imbalance and variation in electrode interface impedance. Custom-designed variable gain amplifiers (ASIC) are used as part of the recording setup. The circuit gain and balance is set by the timing of microcontroller generated clock signals. Measured results are presented which confirm that improved common-mode rejection is achieved compared to a single differential amplifier in the presence of input network imbalance. Practical measured examples further validate gain control suitable for biopotential recording and power-line rejection for wearable ECG and EMG recording. The prototype front-end consumes 318 μW including amplifiers and microcontroller.

Influence of unit distance and conduction velocity on the spectra of extracellular action potentials recorded with intrafascicular electrodes

The use of highly selective penetrating electrodes yields multi-unit extracellular action potential (AP) recordings of the nerve fibers in the vicinity of the electrode. Accessing the information carried within the neural data stream further requires discrimination and separation of the multi-unit recording into their constituent multiple single unit spike trains. Shape differences in the single fiber action potentials (SFAPs) are typically used as the criteria for unit separation. The present paper explores the origins of the shape differences through analysis of the SFAP in the frequency domain. We present the derivation and computational model predictions of a method to quantitatively analyse changes in the spectral components of SFAPs with an axially located intrafascicular electrode with non-radially symmetrical sensitivity function. A spatial tissue filter relationship was derived using reciprocity equations in the spatial frequency domain and transformed to time frequency. A three dimensional bioelectrical volume conductor finite element model of a recording electrode residing in a nerve fascicle was developed to explore the potential distribution in the nerve fascicle and further derive the electrode-fiber coupling function in the time-frequency domain. It was found that the spectral distribution of the SFAP was multimodal in nature, similar to empirical reported earlier, and could be predicted by taking the single fiber action currents (SFACs) filtered by the electrode-fiber coupling function. This function manifested itself as a low-pass filter of the SFAC, dependent upon the fiber's location relative to the electrode and conduction velocity. Analysis of the spectral distribution revealed that changes in the landmarks of the distribution could be related to the fiber location and conduction velocity. Moreover, a consistent relationship was found when exploring the distribution of fibers located off the one axis of symmetry.

Variable-gain, low-noise amplification for sampling front ends

This paper presents a low-noise front-end amplifier with configurable gain, targeting the recording of small signals, such as the electrocardiogram (ECG) or electroneurogram (ENG). The circuit consists of a continuous-time input stage using lateral bipolar transistors realized in complementary metal-oxide semiconductor (CMOS) technology followed by a switched-capacitor integrating stage. The voltage gain is adjustable by varying the phase delay between two system clocks. Simulated and measured results for a chip fabricated in 0.35-μm CMOS technology are reported. The amplifier occupies an active area of 0.064 mm(2), yields a nominal gain of 630 V/V with more than a 50-dB tuning range, less than 16 nVrms/√Hz input noise and a common-mode rejection of more than 97 dB. Its power consumption is 280 μW with a ±1.5-V supply.

A summary of the theory of velocity selective neural recording

This paper describes improvements to the technique of velocity selective recording (VSR) in which multiple neural signals are matched and summed to identify excited axon populations in terms of velocity. This form of recording has been termed intrinsic velocity selective recording (IVSR). The signals are acquired using a multi-electrode cuff (MEC) which is now available as a component for use in implantable neuroprostheses. The improvements outlined in the paper involve the use of bandpass filters at the output of the system which allows a higher level of selectivity to be obtained than is possible using IVSR.