Treadmill-based system for postural studies: Design and validation.

Computer-controlled treadmills are common in many gait labs and offer great potential for conducting perturbation-based postural studies. However, the time-course of these disturbances can be too brief to be controlled manually through product software. Here we present a system that combines a Bertec® split-belt treadmill with custom hardware and software to deliver postural disturbances during standing and record data from multiple sources simultaneously. We used this system to administer to 15 healthy participants an 8-session perturbation-based training protocol in which they learned to respond without stepping to progressively larger perturbations. Kinematic, electromyographic, and force data were collected throughout. Motion capture was used to characterize the accuracy and repeatability of the treadmill-delivered perturbations with respect to duration, displacement, and peak velocity. These (observed) data were compared to that expected based on software commands and the known constraints of the treadmill (i.e., 10 Hz operating speed). We found perturbation durations to be as expected. Peak velocities and displacements were slightly higher than expected (average increases were 0.59 cm/s and 1.76 cm, respectively). Because this increase in magnitude was consistent, it did not impede training or affect data analysis. Treadmill behavior was repeatable across 95 % of trials.

The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders.

The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles-e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill-and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway's excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value. To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex-the electrically-elicited analog of the spinal stretch reflex-in the soleus muscle. Down-conditioning this pathway's excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.

Correction to: PyPNS: Multiscale Simulation of a Peripheral Nerve in Python.

The original version of this article unfortunately contained a mistake. The following text: "This project has received funding from European Research Council (ERC) Synergy Grant no. 319818." is missing in the Acknowledgments.

PyPNS: Multiscale Simulation of a Peripheral Nerve in Python.

Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

Retraining Reflexes: Clinical Translation of Spinal Reflex Operant Conditioning.

Neurological disorders, such as spinal cord injury, stroke, traumatic brain injury, cerebral palsy, and multiple sclerosis cause motor impairments that are a huge burden at the individual, family, and societal levels. Spinal reflex abnormalities contribute to these impairments. Spinal reflex measurements play important roles in characterizing and monitoring neurological disorders and their associated motor impairments, such as spasticity, which affects nearly half of those with neurological disorders. Spinal reflexes can also serve as therapeutic targets themselves. Operant conditioning protocols can target beneficial plasticity to key reflex pathways; they can thereby trigger wider plasticity that improves impaired motor skills, such as locomotion. These protocols may complement standard therapies such as locomotor training and enhance functional recovery. This paper reviews the value of spinal reflexes and the therapeutic promise of spinal reflex operant conditioning protocols; it also considers the complex process of translating this promise into clinical reality.

Influence of Cholecystokinin-8 on Compound Nerve Action Potentials from Ventral Gastric Vagus in Rats.

OBJECTIVE: Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for a number of conditions, such as epilepsy, depression and for Neurometabolic Therapies, especially for treating obesity. The objective of this study was to characterize the left ventral subdiaphragmatic gastric trunk of vagus nerve (SubDiaGVN) and to analyze the influence of intravenous injection of gut hormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observed on the same branch, with the aim of understanding the impact of hormones on VNS and incorporating the methods and results into closed loop implant design. METHODS: The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulated with electric current and the elicited CNAPs were recorded on the SubDiaGVN under four different conditions: Control (no injection), Saline, CCK1 (100[Formula: see text]pmol/kg) and CCK2 (1000[Formula: see text]pmol/kg) injections. RESULTS: We identified the presence of A[Formula: see text], B, C1, C2, C3 and C4 fibers with their respective velocity ranges. Intravenous administration of CCK in vivo results in selective, statistically significant reduction of CNAP components originating from A and B fibers, but with no discernible effect on the C fibers in [Formula: see text] animals. The affected CNAP components exhibit statistically significant ([Formula: see text] and [Formula: see text]) higher normalized stimulation thresholds. CONCLUSION: This approach of characterizing the vagus nerve can be used in closed loop systems to determine when to initiate VNS and also to tune the stimulation dose, which is patient-specific and changes over time.

Extracellular pH monitoring for use in closed-loop vagus nerve stimulation.

OBJECTIVE: Vagal nerve stimulation (VNS) has shown potential benefits for obesity treatment; however, current devices lack physiological feedback, which limit their efficacy. Changes in extracellular pH (pHe) have shown to be correlated with neural activity, but have traditionally been measured with glass microelectrodes, which limit their in vivo applicability. APPROACH: Iridium oxide has previously been shown to be sensitive to fluctuations in pH and is biocompatible. Iridium oxide microelectrodes were inserted into the subdiaphragmatic vagus nerve of anaesthetised rats. Introduction of the gut hormone cholecystokinin (CCK) or distension of the stomach was used to elicit vagal nerve activity. MAIN RESULTS: Iridium oxide microelectrodes have sufficient pH sensitivity to readily detect changes in pHe associated with both CCK and gastric distension. Furthermore, a custom-made Matlab script was able to use these changes in pHe to automatically trigger an implanted VNS device. SIGNIFICANCE: This is the first study to show pHe changes in peripheral nerves in vivo. In addition, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pHe associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.

Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals.

This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors' previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 µV (mean), 0.63 µV (median) and 0.6 µV (standard deviation) per heartbeat on a 1 mVp-p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 µV (mean), 8.5 µV (median) and 9.0 µV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 µV, 11.6 µV (mean), 7.8 µV, 8.9 µV (median) and 9.8 µV, 9.3 µV (standard deviation) per heartbeat.

An array of individually addressable micro-needles for mapping pH distributions.

This work describes the preparation of an array of individually addressable pH sensitive microneedles which are sensitized by electrodepositing iridium oxide. The impact of the deposition potential, storage conditions and interferents on the sensor characteristics such as slope, offset, intra- and inter-batch reproducibility is investigated. The device may be a useful tool for carrying out direct pH measurements of soft and heterogeneous samples such as tissues and organs. For example, we demonstrated that the microneedle array can be employed for real-time mapping of the cardiac pH distribution during cycles of global ischemia and reperfusion.

A low-noise instrumentation amplifier with DC suppression for recording ENG signals.

This paper presents an AC-coupled instrumentation amplifier for electroneurogram (ENG) activity recording. For this design, we evaluate gain and noise requirements based on interference sources (electrodes, power line, EMG). The circuit has been implemented in a commercially-available 0.35µm CMOS technology with total power consumption 460µW. The amplifier achieves CMRR 107 dB and integrated input referred noise 940 nV. The gain is 63 dB and the bandwidth is 0.5 Hz- 13 kHz. The chosen topology enables to minimise on-chip capacitance (only 27 pF), with a total chip area of 0.4mm2.

Fiber size-selective stimulation using action potential filtering for a peripheral nerve interface: A simulation study.

Functional electrical stimulation is a powerful tool for restoration of function after nerve injury. However selectivity of stimulation remains an issue. This paper presents an alternative stimulation technique to obtain fiber size-selective stimulation of nerves using FDA-approved electrode implants. The technique was simulated for the ventral roots of Xenopus Laevis, motivated by an application in bladder control. The technique relies on applying a high frequency alternating current to filter out action potentials in larger fibers, resulting in selective stimulation of the smaller fibers. Results predict that the technique can distinguish fibers with only a 2 µm difference in diameter (for nerves not exceeding 2mm in diameter). The study investigates the behaviour of electrically blocked nerves in detail. Model imperfections and simplifications yielded some artefacts in the results, as well as unexpected nerve behaviour which is tentatively explained.

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.

Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

An analogue front-end model for developing neural spike sorting systems.

In spike sorting systems, front-end electronics is a crucial pre-processing step that not only has a direct impact on detection and sorting accuracy, but also on power and silicon area. In this work, a behavioural front-end model is proposed to assess the impact of the design parameters (including signal-to-noise ratio, filter type/order, bandwidth, converter resolution/rate) on subsequent spike processing. Initial validation of the model is provided by applying a test stimulus to a hardware platform and comparing the measured circuit response to the expected from the behavioural model. Our model is then used to demonstrate the effect of the Analogue Front-End (AFE) on subsequent spike processing by testing established spike detection and sorting methods on a selection of systems reported in the literature. It is revealed that although these designs have a wide variation in design parameters (and thus also circuit complexity), the ultimate impact on spike processing performance is relatively low (10-15%). This can be used to inform the design of future systems to have an efficient AFE whilst also maintaining good processing performance.

Minimum requirements for accurate and efficient real-time on-chip spike sorting.

BACKGROUND: Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e., a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting. NEW METHOD: We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching. RESULTS: We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. COMPARISON WITH EXISTING METHODS: A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data. CONCLUSIONS: Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.

Computationally efficient feature denoising filter and selection of optimal features for noise insensitive spike sorting.

Feature extraction is a critical step in real-time spike sorting after a spike is detected. Features should be informative and noise insensitive for high classification accuracy. This paper describes a new feature extraction method that utilizes a feature denoising filter to improve noise immunity while preserving spike information. Six features were extracted from filtered spikes, including a newly developed feature, and a separability index was applied to select optimal features. Using a set of the three highest-performing features, which includes the new feature, this method can achieve spike classification error as low as 5% for the worst case noise level of 0.2. The computational complexity is only 11% of principle component analysis method and it only costs nine registers per channel.

Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting.

Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation.