Electromyogram Interference Reduction In Neural Signal Recording Using Simple RC Compensation Circuits

Neuroprosthesis can partially restore lost motor functionalities of individuals such as bladder voiding using functional electrical stimulation (FES) techniques. FES involves applying pattern of electrical current pulses using implanted electrodes to trigger affected nerves that are damaged due to paralysis. A neural signal recorded using tripolar cuff electrodes is significantly contaminated due to the presence of EMG interference from the surrounding muscles. Conventional neural amplifiers are unable to remove such interferences and modifications to the design are required. The modification to the design of the Quasi-tripole (QT) amplifier is considered in this work to minimise the EMG interferences from neural signal recording. The analogy between this modified version of QT known as mQT and Wheatstone bridge claims to neutralise the EMG interference by adding compensation circuit to either end of the outer electrodes of the tripolar cuff and therefore balancing the bridge. In this work, we present simple 3 and 2 stage RC compensation circuits to minimise EMG interference in trying to balance the bridge in the neural frequency band of interest (500-10kHz). It is shown that simple RC compensation circuit in series reduces EMG interference only at the spot frequency rather than linearly in the entire frequency band of interest. However, two and three stages RC ladder compensation circuits mimicking electrode-electrolyte interface, can minimize the EMG interference linearly in the entire frequency band of interest, without requiring any readjustment to their components. The aim is to minimise EMG interference as close to null as possible. Invitro testing of about 20% imbalanced cuff electrode with proposed 3 and 2 stage RC ladder compensation circuits resulted in linear EMG interference reduction atleast by a factor of 6. On an average, this yielded an improvement of above 80% EMG minimisation, in contrast to above 90% observed in the optimisation results, when 1Ω transimpedance (EMG) was introduced into the setup. Further improvements to the setup and design can give more promising results in reliable neural signal recording for FES applications

Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider

A new method for spike extraction using velocity selective recording demonstrated with physiological ENG in Rat

BACKGROUND: This paper describes a series of experiments designed to verify a new method of electroneurogram (ENG) recording that enables the rate of neural firing within prescribed bands of propagation velocity to be determined in real time. Velocity selective recording (VSR) has been proposed as a solution to the problem of increasing the information available from an implantable neural interface (typically with electrodes in circumferential nerve cuffs) and has been successful in transforming compound action potentials into the velocity domain. NEW METHOD: The new method extends VSR to naturally-evoked (physiological) ENG in which the rate of neural firing at particular velocities is required in addition to a knowledge of the velocities present in the recording. RESULTS: The experiments, carried out in rats required individual spikes to be distinct and non-overlapping, which could be achieved by a microchannel or small-bore cuff. In these experiments, strands of rat nerve were laid on ten hook electrodes in oil to demonstrate the principle. COMPARISON WITH EXISTING METHOD: The new method generates a detailed overview of the firing rates of neurons based on their conduction velocity and direction of propagation. In addition it allows real time working in contrast to existing spike sorting methods using statistical pattern processing techniques. CONCLUSIONS: Results show that by isolating neural activity based purely on conduction velocity it was possible to determine the onset of direct cutaneous stimulation of the L5 dermatome

Towards Natural Control of Artificial Limbs

The use of implantable electrodes has been long thought as the solution for a more natural control of artificial limbs, as these offer access to long-term stable and physiologically appropriate sources of control, as well as the possibility to elicit appropriate sensory feedback via neurostimulation. Although these ideas have been explored since the 1960’s, the lack of a long-term stable human-machine interface has prevented the utilization of even the simplest implanted electrodes in clinically viable limb prostheses.In this thesis, a novel human-machine interface for bidirectional communication between implanted electrodes and the artificial limb was developed and clinically implemented. The long-term stability was achieved via osseointegration, which has been shown to provide stable skeletal attachment. By enhancing this technology as a communication gateway, the longest clinical implementation of prosthetic control sourced by implanted electrodes has been achieved, as well as the first in modern times. The first recipient has used it uninterruptedly in daily and professional activities for over one year. Prosthetic control was found to improve in resolution while requiring less muscular effort, as well as to be resilient to motion artifacts, limb position, and environmental conditions.In order to support this work, the literature was reviewed in search of reliable and safe neuromuscular electrodes that could be immediately used in humans. Additional work was conducted to improve the signal-to-noise ratio and increase the amount of information retrievable from extraneural recordings. Different signal processing and pattern recognition algorithms were investigated and further developed towards real-time and simultaneous prediction of limb movements. These algorithms were used to demonstrate that higher functionality could be restored by intuitive control of distal joints, and that such control remains viable over time when using epimysial electrodes. Lastly, the long-term viability of direct nerve stimulation to produce intuitive sensory feedback was also demonstrated.The possibility to permanently and reliably access implanted electrodes, thus making them viable for prosthetic control, is potentially the main contribution of this work. Furthermore, the opportunity to chronically record and stimulate the neuromuscular system offers new venues for the prediction of complex limb motions and increased understanding of somatosensory perception. Therefore, the technology developed here, combining stable attachment with permanent and reliable human-machine communication, is considered by the author as a critical step towards more functional artificial limbs

Classification of Sensory Neural Signals through Deep Learning Methods

The recording and analysis of peripheral neural signals can be beneficial to provide feedback to prosthetic limbs and recover the sensory functionality in people with nerve injuries. Nevertheless, the interpretation of sensory recordings extracted from the nerve is not trivial, and only few studies have applied classifiers on sequences of neural signals without previous feature extraction. This paper evaluates the classification performance of two deep learning (DL) models (CNN and ConvLSTM) applied to the electroneurographic (ENG) activity recorded from the sciatic nerve of rats. The ENG signals, available from two public datasets, were recorded using multi-channel cuff electrodes in response to four sensory inputs (plantarflexion, dorsiflexion, nociception, and touch) elicited in response to mechanical stimulation applied to the hind paw of the rats. Different temporal lengths of the signals were considered (2.5 s, 1 s, 500 ms, 200 ms, and 100 ms), Both the two DL models proved to correctly discriminate sensory stimuli without the need of hand-engineering feature extraction. Moreover, ConvLSTM outperformed state-of-the-art results in classifying sensory ENG activity (more than 90% F1-score for sequences greater than 500 ms), and it showed promising results for real-time application scenarios

Offset prediction for charge-balanced stimulus waveforms

Functional electrical stimulation with cuff electrodes involves the controlled injection of current into an electrically excitable tissue for sensory or motor rehabilitation. Some charge injected during stimulation is 'lost' at the electrode-electrolyte interface when the charge carrier is translated from an electron to an ion in the solution. The process of charge injection through chemical reactions can reduce electrode longevity and implant biocompatibility. Conventionally, the excess charge is minimized by complex hardware solutions, which are often not appropriate for robust long-term implantable solutions. Here, we present a method of waveform design that minimizes irrecoverable charge during continuous pulsing through the use of biphasic waveforms with unequally charged phases. We developed an equivalent electrical model of the electrode-electrolyte impedance based on the electrode's surface chemistry during psuedo-bipolar stimulation conditions. Simulations with the equivalent circuit determined the uncompensated charge to be a function of stimulus parameters. In vitro stimulation experiments in saline confirmed that we could preemptively compensate for the excess charge following biphasic stimulus waveforms. As a result, there was a 92% reduction in the pre-pulse potential after a pulse train with this new waveform design when compared to stimulation with conventional biphasic waveforms

Neuromodulation in Experimetal Animal Models of Epilepsy

Epilepsy is the most common serious brain disorder affecting 0.5-1% of the general population. This neurological disorder consists of recurrent seizures, resulting from excessive, uncontrolled electrical activity in the brain. Despite the pharmacological development of new treatments, still one third of the epilepsy patients does not respond sufficiently to anti-epileptic drugs (AED) and are called refractory patients. Hence, there is a constant impetus to search for other treatment strategies like epilepsy surgery, vagus nerve stimulation and deep brain stimulation. Besides the ongoing research on the efficacy of anti-epileptic treatments in suppressing seizures (anti-seizure effect), we want to seek for therapies that can lead to plastic changes in the epileptic network and in this way have a modulating effect. The impact of such therapies cannot be overlooked, because they may slow down processes underlying epilepsy, might prevent or even cure epilepsy. Neuropharmacological therapy with levetiracetam (LEV) and vagus nerve stimulation (VNS) are two novel treatments for refractory epilepsy. Acute application of both treatment options can be very effective. LEV can act rapidly on seizures in both animals and humans. In addition, preclinical studies suggest that LEV may have anti-epileptogenic and neuroprotective effects, with the potential to slow or arrest disease progression. VNS as well can have an immediate effect on seizures in animals and patients with in addition a cumulative effect after prolonged treatment. Studies in man are hampered by the heterogeneity of patient populations (age, course of the epilepsy, type of epilepsy, AED regime and genetic background) and the difficulty to study therapy-related effects in a systematic way. Therefore, investigation was performed utilizing two models mimicking epilepsy in humans. They are both chronic models with seizures evolving from true, genetically-driven epileptogenesis. Genetic absence epilepsy rats from Strasbourg (GAERS) have inborn absence epilepsy and Fast rats have a genetically determined sensitivity for electrical amygdala kindling, which is an excellent model of temporal lobe epilepsy. Our findings support the hypothesis that these treatments can be considered as neuromodulatory: changes are induced in central nervous system function or organization as a result of influencing and initiating neurophysiological signals

Analysing vagus nerve spontaneous activity using finite element modelling

Objective. Finite element modelling has been widely used to understand the effect of stimulation on the nerve fibres. Yet the literature on analysis of spontaneous nerve activity is much scarcer. In this study, we introduce a method based on a finite element model, to analyse spontaneous nerve activity with a typical bipolar electrode recording setup, enabling the identification of spontaneously active fibres. We applied our method to the vagus nerve, which plays a key role in refractory epilepsy. Approach. We developed a 3D model including dynamic action potential propagation, based on the vagus nerve geometry. The impact of key recording parameters – inter-electrode distance and temperature – and uncontrolled parameters – fibre size and position in the nerve – on the ability to discriminate active fibres were quantified. A specific algorithm was implemented to detect and classify action potentials from recordings and tested on six rats in vivo vagus nerve recordings. Main results. Fibre diameters can be discriminated if they are below 3 µm and 7 µm, respectively for inter-electrode distances of 2 mm and 4 mm. The impact of the position of the fibre inside the nerve on fibre diameter discrimination, is limited. The range of active fibres identified by modelling in the vagus nerve of rats is in agreement with ranges found at histology. Significance. The nerve fibre diameter, directly proportional to the action potential propagation velocity, is related to a specific physiological function. Estimating the source fibre diameter is thus essential to interpret neural recordings. Among many possible applications, the present method was developed in the context of a project to improve vagus nerve stimulation therapy for epilepsy