A polyimide pressure-contact multielectrode array for implantation along a submillimeter neural process in small animals

We describe a microelectrode system for recordings from nerve bundles with diameters ranging from 20-200 microm. A novel polyimide structure allows for a planar microfabricated device to constrain a free neural process against several recording sites. This polyimide array contains multiple zigzag vias through which a small nerve process may be woven while remaining functionally intact in a live preparation. Our electrode construct features the benefits of nerve cuffs (evenly spaced electrodes in a polymer) and the functionality of a nerve hook (ability to connect to submillimeter processes). The device records extracellular action potentials in the red-swamp crayfish, Procambarus clarkii. Action potential propagation is monitored at several sites along a constrained nerve in this model organism's peripheral nervous system. Details of temporal accuracy and error sources in absolute conduction velocity measurements from microelectrode arrays are discussed.     

Estimation of motor unit conduction velocity from surface EMG recordings by signal-based selection of the spatial filters

Muscle fiber conduction velocity (CV) can be estimated by the application of a pair of spatial filters to surface electromagnetic (EMG) signals and compensation of the spatial filter transfer function with equivalent temporal filters. This method integrates the selection of the spatial filters for signal detection to the estimation of CV. Using this approach, in this paper, we propose a novel technique for signal-based selection of the spatial filter pair that minimizes the effect of nonpropagating signal components (end-of-fiber effects) on CV estimates (optimal filters). The technique is applicable to signals with one propagating and one nonpropagating component, such as single motor unit action potentials. It is shown that the determination of the optimal filters also allows the identification of the propagating and nonpropagating signal components. The new method was applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were recorded from the abductor pollicis brevis with a linear array of 16 electrodes. In the simulations, the proposed approach provided CV estimates with lower bias due to nonpropagating signal components than previously proposed methods based on the entire signal waveform. In the experimental signals, the technique separated propagating and nonpropagating signal components with an average reconstruction error of 2.9 +/- 0.9% of the signal energy. The technique may find application in single motor unit studies for decreasing the variability and bias of CV estimates due to the presence and different weights of the nonpropagating components.     

Conversion of magnetocardiographic recordings between two different multichannel SQUID devices

Comparison of biomagnetic measurements performed with different multichannel magnetometers is difficult, because differing sensor types and locations do not allow measurements from the same locations in respect to the body. In this study, two transformation procedures were utilized to compare magnetocardiograms (MCG) recorded with two different multisensor systems. Signals from one sensor array were used to compute parameters of a multipole expansion or minimum-norm estimates at 1-ms steps over the cardiac cycle. The signals of the second sensor array were then simulated from the computed estimates and compared against measured data. Both the multipole- and the minimum-norm-based transformation method yielded good results; the average correlation between simulated and measured signals was 93%. Thus, the methods are useful to compare MCG recordings performed using differing sensor configurations, e.g., for multicenter patient studies. This study provides the first empirical basis for assessing the transformation of MCG data of differing devices by general model-based field reconstructions.     

A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface EMG signals

We describe a new method for the estimation of muscle fiber conduction velocity (CV) from surface electromyography (EMG) signals. The method is based on the detection of two surface EMG signals with different spatial filters and on the compensation of the spatial filtering operations by two temporal filters (with CV as unknown parameter) applied to the signals. The transfer functions of the two spatial filters may have different magnitudes and phases, thus the detected signals have not necessarily the same shape. The two signals are first spatially and then temporally filtered and are ideally equal when the CV value selected as a parameter in the temporal filters corresponds to the velocity of propagation of the detected action potentials. This approach is the generalization of the classic spectral matching technique. A theoretical derivation of the method is provided together with its fast implementation by an iterative method based on the Newton's method. Moreover, the lowest CV estimate among those obtained by a number of filter pairs is selected to reduce the CV bias due to nonpropagating signal components. Simulation results indicate that the method described is less sensitive than the classic spectral matching approach to the presence of nonpropagating signals and that the two methods have similar standard deviation of estimation in the presence of additive, white, Gaussian noise. Finally, experimental signals have been collected from the biceps brachii muscle of ten healthy male subjects with an adhesive linear array of eight electrodes. The CV estimates depended on the electrode location with positive bias for the estimates from electrodes close to the innervation or tendon regions, as expected. The proposed method led to significantly lower bias than the spectral matching method in the experimental conditions, confirming the simulation results.     

Proposal of a new method for narrowing and moving the stimulated region of cochlear implants: animal experiment and numerical analysis

We have proposed the tripolar electrode stimulation method (TESM) for narrowing the stimulation region and continuously moving the stimulation site for cochlear implants. The TESM stimulates the auditory nerve array using three adjacent electrodes which are selected among the electrodes of an electrode array within the lymphatic fluid. Current is emitted from each of the two lateral electrodes and received by the central electrode. The current received by the central electrode is made equal to the sum of the currents emitted from the lateral electrodes. In this paper, we evaluate whether or not TESM works according to a theory which is based on numerical analysis using an electrical equivalent circuit model of the auditory nerve fibers. In this simulation, the sums of the excited model fibers are compared to the compound action potentials (CAP's) which we obtained through animal experiments. To identify the main parameter while maintaining the amplitude of the CAP (the sum of the fired fibers), we assumed the presence of some parameters from the radial current density profile. In the case of the width value among the parameters being kept constant, the amplitude of the CAP was almost constant; thus, the number of the fired fibers was also almost constant. The width value equals the distance between the points at which the profile of the radial current density of the electrode array and the line of the radial threshold current density of the electrode array intersect. It is possible to determine the measure of the stimulation region or site by controlling the width value and the ratios of the currents emitted from the lateral electrodes. As a result, we succeeded in narrowing the stimulation region by controlling the sum of the currents emitted from the two lateral electrodes. Also we succeeded in continuously moving the stimulation site by modifying the currents emitted from the two lateral electrodes.     

Information contained in sensory nerve recordings made withintrafascicular electrodes

Multiunit recordings were made in anesthetized cats with chronically implanted intrafascicular electrodes over a period of six months. Neural signals recorded with these electrodes consisted of activity in sensory fibers innervating a variety of cutaneous mechanoreceptors. Mechanical stimuli were used to selectively activate individual nerve fibers, and the receptive field and receptor type were identified for each unit. Over a period of six months, there was a net shift in the recorded population, but the electrodes continued to provide a representative sample of the activity in the fascicle as a whole. The total number of units from which activity could be recorded remained roughly constant with time, and individual units persisted in the recordings for up to six months. These results indicate that intrafascicular electrodes could be used to sample information carried by individual somatosensory fibers on a long term basis.     

Sensory nerve recording for closed-loop control to restore motorfunctions

A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation the authors designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. They used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications     

Artefact reduction with alternative cuff configurations

In nerve cuff electrode recordings of neural signals, the pick-up of interfering signals can be reduced by choosing appropriate cuff configurations. In the traditionally used tripolar configuration, short circuiting of the end electrodes is expected to reduce the field inside the cuff from interfering signals. A model study suggests that moving the end electrodes toward the center of the cuff reduces the pick-up of interfering signals. In this paper, these properties are studied in more detail using a rabbit model. In addition, a new cuff configuration is suggested, which has an additional set of short circuited end electrodes. The total improvement of signal-to-noise ratio in the new configuration as compared with the traditionally used tripolar configuration was 73% for muscle signals and 127% for the stimulus pulse.     

Noncausal 2-D spectrum estimation for direction finding

A two-dimensional noncausal autoregressive (NCAR) plus additive noise model-based spectrum estimation method is presented for planar array data typical of signals encountered in array processing applications. Since the likelihood function for NCAR plus noise data is nonlinear in the model parameters and is further complicated by the unknown variance of the additive noise, computationally intensive gradient search algorithms are required for computing the estimates. If a doubly periodic lattice is assumed, the complexity of the approximate maximum likelihood (ML) equation is significantly reduced without destroying the theoretical asymptotic properties of the estimates and degrading the observed accuracy of the estimated spectra. Initial conditions for starting the approximate ML computation are suggested. Experimental results that can be used to evaluate the signal-plus-noise approach and compare its performance to those of signal-only methods are presented for Gaussian and simulated planar array data. Statistics of estimated spectrum parameters are given, and estimated spectra for signals with close spatial frequencies are shown. The approximate ML parameter estimate's asymptotic properties, such as consistency and normality, are established, and lower bounds for the estimate's errors are derived, assuming that the data are Gaussian     

A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.     

Effects of model errors on waveform estimation using the MUSICalgorithm

Sensor arrays are frequently used to separate and reconstruct superimposed signals arriving from different directions. The paper studies the effect of model errors, i.e., differences between the assumed and actual array response, on the quality of the reconstructed signals. Model errors are the limiting factor of array performance when the observation time is sufficiently long. The authors analyze a signal estimation technique which is based on the MUSIC algorithm. Formulas are derived for the signal-to-interference and signal-to-noise ratios as function of the model errors. By evaluating these formulas for selected test cases they gain some insights into the sensitivity of the signal estimation problem to model uncertainty     

The effects of array calibration errors on DF-based signal copyperformance

This paper studies the effect of array calibration errors on the performance of various direction finding (DF) based signal copy algorithms. Unlike blind copy methods, this class of algorithms requires an estimate of the directions of arrival (DOAs) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is sufficiently long, the following algorithms are studied: classical beamforming, least squares, total least squares, linearly constrained minimum variance beamforming, and structured stochastic estimation. Expressions for the mean-square error of the signal estimates are derived as a function of the calibration errors for both the case where the DOAs are known precisely and for the case where the DOAs must be estimated     

Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity

A platform for high spatial and temporal resolution electrophysiological recordings of in vitro electrogenic cell cultures handling 4096 electrodes at a full frame rate of 8 kHz is presented and validated by means of cardiomyocyte cultures. Based on an active pixel sensor device implementing an array of metallic electrodes, the system provides acquisitions at spatial resolutions of 42 mum on an active area of 2.67 mm times 2.67 mm, and in the zooming mode, temporal resolutions down to 8 mus on 64 randomly selected electrodes. The low-noise performances of the integrated amplifier (11 muV_rms) combined with a hardware implementation inspired by image/video processing concepts enable high-resolution acquisitions with real-time preprocessing capabilities adapted to the handling of the large amount of acquired data.     

The nerve-electrode interface of the cochlear implant: currentspread

One of the fundamental facets of the cochlear implant that must be understood to predict accurately the effect of an electrical stimulus on the auditory nerve is the nerve-electrode interface. One aspect of this interface is the degree to which current delivered by an electrode spreads to neurons distant from it. This paper reports a direct mapping of this current spread using recordings from single units from the cat auditory nerve. Large variations were seen in the degree to which the different units are selective in responding to electrodes at different positions within the scala tympani. Three types of units could be identified based on the selectiveness of their response to the different electrodes in a linear array. The first type of unit exhibited a gradual increase in threshold as the stimulating site was moved from more apical to more basal locations within the scala tympani. The second type of unit exhibited a sharp local minimum, with rapid increases in threshold in excess of 6 dB/mm in the vicinity of the minimum. At electrode sites distant from the local minima the rate of change of the threshold approached that of the first type of units. The final type of unit also demonstrated a gradual change in threshold with changing electrode position, however, two local minima, one apical and one basal, could be identified. These three types are hypothesized to correspond to units which originate apical to the electrode array, along the electrode array and basal to the electrode array     

一种波达方向和幅相误差联合估计的改进方法

阵列幅相误差的存在将大大影响波达方向的估计精度,反之在波达方向未知的情况下,幅相误差同样无法精确估计,两者相互耦合。在详细分析两者耦合情况的基础上,从工程应用角度出发,给出了一种改进的幅相误差校正方法。该方法能够在波达方向未知的条件下,精确估计阵列幅相误差,同时给出波达方向。由于仅需一次迭代,该方法的计算量较少,更易实现。仿真数据的处理结果证明了该方法的可行性。     

Direction of arrival estimation using parametric signal models

We consider the problem of estimating the directions-of-arrival (DOAs) of narrowband sources with known center frequency. The paper evaluates the potential improvement in estimation accuracy by using spatial-temporal processing for signals obeying a deterministic parametric model. One would expect that prior information about the temporal structure of the signals will yield some gain in performance. By deriving the Cramer-Rao bound (CRB) on the DOA estimates, we quantify this gain and identify the cases for which the gain is significant. We show that for the single-source case, spatial-temporal processing does not yield any gain in performance relative to conventional spatial processing. For multiple noncoherent signals, incorporating temporal processing can achieve the single-source performance, yielding a significant gain for the case of multiple sources with small spatial separation relative to the beamwidth of the array. However, spatial-temporal processing cannot yield any gain in performance for multiple coherent signals     

Weighted subspace fitting for general array error models

Model error sensitivity is an issue common to all high-resolution direction-of-arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to array calibration errors have also appeared in the literature. Herein, one such approach is adopted that assumes that the errors due to finite samples and model errors are of comparable size. A weighted subspace fitting method for very general array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly, the method reduces to the WSF (MODE) estimator if no model errors are present. Vice versa, assuming that model errors dominate, the method specializes to the corresponding “model-errors-only subspace fitting method.” Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated. The paper also contains a large sample analysis of one of the alternative methods, namely, MAPprox. It is shown that MAPprox also provides minimum variance estimates under reasonable assumptions     

Deconvolution and wavelet-based methods for membrane current estimation from simulated fractionated electrograms

In infarcted myocardium, extracellular recordings exhibit multiple deflections due to irregular pathway of the electric impulse. In this work the problem of distinguishing local from distant deflections is tackled. In order to evaluate the proposed methods in a controlled setting, simulated data are used, following both Beeler-Reuter and Luo-Rudy kinetics. The input is an array of electrograms positioned on grid-points of a rectangular grid and the output is an array of estimates of the membrane current. First, deconvolution techniques are used in the form of spatial filtering for membrane current estimation from the extracellular recordings. Second, the extracellular recordings undergo wavelet based transformation, followed by a spatial filter which enhances local activity deflections and suppresses distant activity deflections. It is shown that wavelet filtering of the extracellular recordings acts as an evaluator of the efficiency of the deconvolution techniques for the membrane current estimation. Subsequently, activation times based on the results from the two methods are used for the reconstruction of the propagation pattern in a zig-zag case in two-dimensional grids. It is shown that the wavelet-based method is more robust, and can work well even in cases where the grid interval in the y direction is four times larger than the single cell size.