Showing papers in "Journal of Neural Engineering in 2006"

A comparison of classification techniques for the P300 Speller

Abstract: This study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.

Chronic neural recordings using silicon microelectrode arrays electrochemically deposited with a poly(3,4-ethylenedioxythiophene) (PEDOT) film

Abstract: Conductive polymer coatings can be used to modify traditional electrode recording sites with the intent of improving the long-term performance of cortical microelectrodes. Conductive polymers can drastically decrease recording site impedance, which in turn is hypothesized to reduce thermal noise and signal loss through shunt pathways. Moreover, conductive polymers can be seeded with agents aimed at promoting neural growth toward the recording sites or minimizing the inherent immune response. The end goal of these efforts is to generate an ideal long-term interface between the recording electrode and surrounding tissue. The goal of this study was to refine a method to electrochemically deposit surfactant-templated ordered poly(3,4-ethylenedioxythiophene) (PEDOT) films on the recording sites of standard 'Michigan' probes and to evaluate the efficacy of these modified sites in recording chronic neural activity. PEDOT-coated site performance was compared to control sites over a six-week evaluation period in terms of impedance spectroscopy, signal-to-noise ratio, number of viable unit potentials recorded and local field potential recordings. PEDOT sites were found to outperform control sites with respect to signal-to-noise ratio and number of viable unit potentials. The benefit of reduced initial impedance, however, was mitigated by the impedance contribution of typical silicon electrode encapsulation. Coating sites with PEDOT also reduced the amount of low-frequency drift evident in local field potential recordings. These findings indicate that electrode sites electrochemically deposited with PEDOT films are suitable for recording neural activity in vivo for extended periods. This study also provided a unique opportunity to monitor how neural recording characteristics develop over the six weeks following implantation.

Towards adaptive classification for BCI.

Abstract: Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain–computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.

Role of electrode design on the volume of tissue activated during deep brain stimulation

Abstract: Deep brain stimulation (DBS) is an established clinical treatment for a range of neurological disorders. Depending on the disease state of the patient, different anatomical structures such as the ventral intermediate nucleus of the thalamus (VIM), the subthalamic nucleus or the globus pallidus are targeted for stimulation. However, the same electrode design is currently used in nearly all DBS applications, even though substantial morphological and anatomical differences exist between the various target nuclei. The fundamental goal of this study was to develop a theoretical understanding of the impact of changes in the DBS electrode contact geometry on the volume of tissue activated (VTA) during stimulation. Finite element models of the electrodes and surrounding medium were coupled to cable models of myelinated axons to predict the VTA as a function of stimulation parameter settings and electrode design. Clinical DBS electrodes have cylindrical contacts 1.27 mm in diameter (d) and 1.5 mm in height (h). Our results show that changes in contact height and diameter can substantially modulate the size and shape of the VTA, even when contact surface area is preserved. Electrode designs with a low aspect ratio (d/h) maximize the VTA by providing greater spread of the stimulation parallel to the electrode shaft without sacrificing lateral spread. The results of this study provide the foundation necessary to customize electrode design and VTA shape for specific anatomical targets, and an example is presented for the VIM. A range of opportunities exist to engineer DBS systems to maximize stimulation of the target area while minimizing stimulation of non-target areas. Therefore, it may be possible to improve therapeutic benefit and minimize side effects from DBS with the design of target-specific electrodes.

Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion.

Abstract: Long-term integration of neuroprosthetic devices is challenged by reactive responses that compromise the brain–device interface. The contribution of physical insertion parameters to immediate damage is not well described. We have developed an ex vivo preparation to capture real-time images of tissue deformation during device insertion using thick tissue slices from rat brains prepared with fluorescently labeled vasculature. Qualitative and quantitative assessments of damage were made for insertions using devices with different tip shapes inserted at different speeds. Direct damage to the vasculature included severing, rupturing and dragging, and was often observed several hundred micrometers from the insertion site. Slower insertions generally resulted in more vascular damage. Cortical surface features greatly affected insertion success; insertions attempted through pial blood vessels resulted in severe tissue compression. Automated image analysis techniques were developed to quantify tissue deformation and calculate mean effective strain. Quantitative measures demonstrated that, within the range of experimental conditions studied, faster insertion of sharp devices resulted in lower mean effective strain. Variability within each insertion condition indicates that multiple biological factors may influence insertion success. Multiple biological factors may contribute to tissue distortion, thus a wide variability was observed among insertions made under the same conditions.

Seperability of four-class motor imagery data using independent components analysis

Abstract: This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.

Brain micromotion around implants in the rodent somatosensory cortex

Abstract: The magnitude of brain tissue micromotion relative to stationary brain implants and its impact on the viability and function of the surrounding brain tissue due to mechanical stresses is poorly understood. The central goal of this study is to characterize surface micromotion in the somatosensory cortex against stationary cylindrical implants. We used a differential variable reluctance transducer (DVRT) in adult rats (n = 6) to monitor micromotion normal to the somatosensory cortex surface. Experiments were performed both in the presence and in the absence of dura mater and displacement measurements were made at three different locations within craniotomies of two different sizes. In anesthetized rats, pulsatile surface micromotion was observed to be in the order of 10-30 microm due to pressure changes during respiration and 2-4 microm due to vascular pulsatility. Brain displacement values due to respiration were significantly lower in the presence of the dura compared to those without the dura. In addition, large inward displacements of brain tissue between 10-60 microm were observed in n = 3 animals immediately following the administration of anesthesia. Such significant micromotion can impact a wide variety of acute and chronic procedures involving any brain implants, precise neurosurgery or imaging and therefore has to be factored in the design of such procedures.

Nanoscale laminin coating modulates cortical scarring response around implanted silicon microelectrode arrays

Abstract: Neural electrodes could significantly enhance the quality of life for patients with sensory and/or motor deficits as well as improve our understanding of brain functions. However, long-term electrical connectivity between neural tissue and recording sites is compromised by the development of astroglial scar around the recording probes. In this study we investigate the effect of a nanoscale laminin (LN) coating on Si-based neural probes on chronic cortical tissue reaction in a rat model. Tissue reaction was evaluated after 1 day, 1 week, and 4 weeks post-implant for coated and uncoated probes using immunohistochemical techniques to evaluate activated microglia/macrophages (ED-1), astrocytes (GFAP) and neurons (NeuN). The coating did not have an observable effect on neuronal density or proximity to the electrode surface. However, the response of microglia/macrophages and astrocytes was altered by the coating. One day post-implant, we observed an ~60% increase in ED-1 expression near LN-coated probe sites compared with control uncoated probe sites. Four weeks post-implant, we observed an ~20% reduction in ED-1 expression along with an ~50% reduction in GFAP expression at coated relative to uncoated probe sites. These results suggest that LN has a stimulatory effect on early microglia activation, accelerating the phagocytic function of these cells. This hypothesis is further supported by the increased mRNA expression of several pro-inflammatory cytokines (TNF-α, IL-1 and IL-6) in cultured microglia on LN-bound Si substrates. LN immunostaining of coated probes immediately after insertion and retrieval demonstrates that the coating integrity is not compromised by the shear force during insertion. We speculate, based on these encouraging results, that LN coating of Si neural probes could potentially improve chronic neural recordings through dispersion of the astroglial scar.

The dynamic neural field approach to cognitive robotics

Abstract: This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitry underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner's action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping-placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work.

High frequency electrical conduction block of the pudendal nerve

Abstract: A reversible electrical block of the pudendal nerves may provide a valuable method for restoration of urinary voiding in individuals with bladder–sphincter dyssynergia. This study quantified the stimulus parameters and effectiveness of high frequency (HFAC) sinusoidal waveforms on the pudendal nerves to produce block of the external urethral sphincter (EUS). A proximal electrode on the pudendal nerve after its exit from the sciatic notch was used to apply low frequency stimuli to evoke EUS contractions. HFAC at frequencies from 1 to 30 kHz with amplitudes from 1 to 10 V were applied through a conforming tripolar nerve cuff electrode implanted distally. Sphincter responses were recorded with a catheter mounted micro-transducer. A fast onset and reversible motor block was obtained over this range of frequencies. The HFAC block showed three phases: a high onset response, often a period of repetitive firing and usually a steady state of complete or partial block. A complete EUS block was obtained in all animals. The block thresholds showed a linear relationship with frequency. HFAC pudendal nerve stimulation effectively produced a quickly reversible block of evoked urethral sphincter contractions. The HFAC pudendal block could be a valuable tool in the rehabilitation of bladder–sphincter dyssynergia.

Bio-heat transfer model of deep brain stimulation-induced temperature changes

Abstract: There is a growing interest in the use of chronic deep brain stimulation (DBS) for the treatment of medically refractory movement disorders and other neurological and psychiatric conditions. Fundamental questions remain about the physiologic effects of DBS. Previous basic research studies have focused on the direct polarization of neuronal membranes by electrical stimulation. The goal of this paper is to provide information on the thermal effects of DBS using finite element models to investigate the magnitude and spatial distribution of DBS-induced temperature changes. The parameters investigated include stimulation waveform, lead selection, brain tissue electrical and thermal conductivities, blood perfusion, metabolic heat generation during the stimulation and lead thermal conductivity/heat dissipation through the electrode. Our results show that clinical DBS protocols will increase the temperature of surrounding tissue by up to 0.8 °C depending on stimulation/tissue parameters.

Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients

Abstract: Electroencephalogram (EEG) gives researchers a non-invasive way to record cerebral activity. It is a valuable tool that helps clinicians to diagnose various neurological disorders and brain diseases. Blinking or moving the eyes produces large electrical potential around the eyes known as electrooculogram. It is a non-cortical activity which spreads across the scalp and contaminates the EEG recordings. These contaminating potentials are called ocular artifacts (OAs). Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the possible experimental designs and may affect the cognitive processes under investigation. In this paper, a nonlinear time-scale adaptive denoising system based on a wavelet shrinkage scheme has been used for removing OAs from EEG. The time-scale adaptive algorithm is based on Stein's unbiased risk estimate (SURE) and a soft-like thresholding function which searches for optimal thresholds using a gradient based adaptive algorithm is used. Denoising EEG with the proposed algorithm yields better results in terms of ocular artifact reduction and retention of background EEG activity compared to non-adaptive thresholding methods and the JADE algorithm.

Optimizing neurotrophic factor combinations for neurite outgrowth.

Abstract: Most neurotrophic factors are members of one of three families: the neurotrophins, the glial cell-line derived neurotrophic factor family ligands (GFLs) and the neuropoietic cytokines. Each family activates distinct but overlapping cellular pathways. Several studies have shown additive or synergistic interactions between neurotrophic factors from different families, though generally only a single combination has been studied. Because of possible interactions between the neurotrophic factors, the optimum concentration of a factor in a mixture may differ from the optimum when applied individually. Additionally, the effect of combinations of neurotrophic factors from each of the three families on neurite extension is unclear. This study examines the effects of several combinations of the neurotrophin nerve growth factor (NGF), the GFL glial cell-line derived neurotrophic factor (GDNF) and the neuropoietic cytokine ciliary neurotrophic factor (CNTF) on neurite outgrowth from young rat dorsal root ganglion (DRG) explants. The combination of 50 ng ml−1 NGF and 10 ng ml−1 of each GDNF and CNTF induced the highest level of neurite outgrowth at a 752 ± 53% increase over untreated DRGs and increased the longest neurite length to 2031 ± 97 µm compared to 916 ± 64 µm for untreated DRGs. The optimum concentrations of the three factors applied in combination corresponded to the optimum concentration of each factor when applied individually. These results indicate that the efficacy of future therapies for nerve repair would be enhanced by the controlled release of a combination of neurotrophins, GFLs and neuropoietic cytokines at higher concentrations than used in previous conduit designs.

A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

Abstract: The field of brain–machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100–200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

An implantable neuroprosthesis for standing and walking in paraplegia: 5-year patient follow-up

Abstract: We present the results of a five-year patient follow-up after implantation of an original neuroprosthesis The system is able to stimulate both epimysial and neural electrodes in such a way that the complete flexor-extensor chain of the lower limb can be activated without using of the withdrawal reflex We demonstrate that standing and assisted walking are possible, and the results have remained stable for five years Nevertheless, some problems were noted, particularly regarding the muscle response on the epimysial channels Analysis of the electrical behaviour and thresholds indicated that the surgical phase is crucial because of the sensitivity of the functional responses to electrode placement Neural stimulation proved to be more efficient and more stable over time This mode requires less energy and provides more selective stimulation This FES system can be improved to enable balanced standing and less fatiguing gait, but this will require feedback on event detection to trigger transitions between stimulation sequences, as well as feedback to the patient about the state of his lower limbs

Toward a self-deploying shape memory polymer neuronal electrode.

Abstract: The widespread application of neuronal probes for chronic recording of brain activity and functional stimulation has been slow to develop partially due to long-term biocompatibility problems with existing metallic and ceramic probes and the tissue damage caused during probe insertion. Stiff probes are easily inserted into soft brain tissue but cause astrocytic scars that become insulating sheaths between electrodes and neurons. In this communication, we explore the feasibility of a new approach to the composition and implantation of chronic electrode arrays. We demonstrate that softer polymer-based probes can be inserted into the olfactory bulb of a mouse and that slow insertion of the probes reduces astrocytic scarring. We further present the development of a micromachined shape memory polymer probe, which provides a vehicle to self-deploy an electrode at suitably slow rates and which can provide sufficient force to penetrate the brain. The deployment rate and composition of shape memory polymer probes can be tailored by polymer chemistry and actuator design. We conclude that it is feasible to fabricate shape memory polymer-based electrodes that would slowly self-implant compliant conductors into the brain, and both decrease initial trauma resulting from implantation and enhance long-term biocompatibility for long-term neuronal measurement and stimulation.

Selection and parameterization of cortical neurons for neuroprosthetic control.

Abstract: When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

A silicon retina that reproduces signals in the optic nerve

Abstract: Prosthetic devices may someday be used to treat lesions of the central nervous system. Similar to neural circuits, these prosthetic devices should adapt their properties over time, independent of external control. Here we describe an artificial retina, constructed in silicon using single-transistor synaptic primitives, with two forms of locally controlled adaptation: luminance adaptation and contrast gain control. Both forms of adaptation rely on local modulation of synaptic strength, thus meeting the criteria of internal control. Our device is the first to reproduce the responses of the four major ganglion cell types that drive visual cortex, producing 3600 spiking outputs in total. We demonstrate how the responses of our device's ganglion cells compare to those measured from the mammalian retina. Replicating the retina's synaptic organization in our chip made it possible to perform these computations using a hundred times less energy than a microprocessor—and to match the mammalian retina in size and weight. With this level of efficiency and autonomy, it is now possible to develop fully implantable intraocular prostheses.

The effect of cystic cavities on deep brain stimulation in the basal ganglia: a simulation-based study.

Abstract: Although the therapeutic effect of deep brain stimulation (DBS) is well recognized, a fundamental understanding of the mechanisms responsible is still not known. In this study finite element method (FEM) modelling and simulation was used in order to study relative changes of the electrical field extension surrounding a monopolar DBS electrode positioned in grey matter. Due to the frequently appearing cystic cavities in the DBS-target globus pallidus internus, a nucleus of grey matter with and without a cerebrospinal fluid filled cystic cavity was modelled. The position, size and shape of the cyst were altered in relation to the electrode. The simulations demonstrated an electrical field around the active element with decreasing values in the radial direction. A stepwise change was present at the edge between grey and white matters. The cyst increased the radial extension and changed the shape of the electrical field substantially. The position, size and shape of the cyst were the main influencing factors. We suggest that cystic cavities in the DBS-target may result in closely related unexpected structures or neural fibre bundles being stimulated and could be one of the reasons for suboptimal clinical effects or stimulation-induced side effects.

Compact self-wiring in cultured neural networks

Abstract: We present a novel approach for patterning cultured neural networks in which a particular geometry is achieved via anchoring of cell clusters (tens of cells/each) at specific positions. In addition, compact connections among pairs of clusters occur spontaneously through a single non-adherent straight bundle composed of axons and dendrites. The anchors that stabilize the cell clusters are either poly-D-lysine, a strong adhesive substrate, or carbon nanotubes. Square, triangular and circular structures of connectivity were successfully realized. Monitoring the dynamics of the forming networks in real time revealed that the self-assembly process is mainly driven by the ability of the neuronal cell clusters to move away from each other while continuously stretching a neurite bundle in between. Using the presented technique, we achieved networks with wiring regions which are made exclusively of neuronal processes unbound to the surface. The resulted network patterns are very stable and can be maintained for as long as 11 weeks. The approach can be used to build advanced neuro-chips for bio-sensing applications (e.g. drug and toxin detection) where the structure, stability and reproducibility of the networks are of great relevance.

Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.

Abstract: We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C(3)/C(4) electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the probabilistic distances between expansion coefficients corresponding to left and right hand movement imagery. This is followed by a frequency domain clustering procedure in each adapted time segment to maximize the discrimination power of the resulting time-frequency features. Then, the most discriminant features from the resulting arbitrarily segmented time-frequency plane are sorted. A principal component analysis (PCA) step is applied to reduce the dimensionality of the feature space. This reduced feature set is finally fed to a linear discriminant for classification. The online step simply computes the reduced dimensionality features determined by the offline step and feeds them to the linear discriminant. We provide experimental data to show that the method can adapt to physio-anatomical differences, subject-specific and hemisphere-specific motor imagery patterns. The algorithm was applied to all nine subjects of the BCI Competition 2002. The classification performance of the proposed algorithm varied between 70% and 92.6% across subjects using just two electrodes. The average classification accuracy was 80.6%. For comparison, we also implemented an adaptive autoregressive model based classification procedure that achieved an average error rate of 76.3% on the same subjects, and higher error rates than the proposed approach on each individual subject.

Architectures for high-performance FPGA implementations of neural models.

Abstract: As the complexity of neural models continues to increase (larger populations, varied ionic conductances, more detailed morphologies, etc) traditional software-based models have difficulty scaling to reach the performance levels desired. This paper describes the use of FPGAs, or field programmable gate arrays, to easily implement a wide variety of neural models with the performance of custom analogue circuits or computer clusters, the reconfigurability of software, and at a cost rivalling personal computers. FPGAs reach this level of performance by enabling the design of neural models as parallel processed data paths. These architectures provide for a wide range of single-compartment, multi-compartment and population models to be readily converted to FPGA implementations. Generalized architectures are described for the efficient modelling of a first-order, nonlinear differential equation in throughput maximizing or latency minimizing data-path configurations. The homogeneity of population and multicompartment models is exploited to form deep pipelines for improved performance. Limitations of FPGA architectures and future research areas are explored.

Computational model of a primate arm: from hand position to joint angles, joint torques and muscle forces.

Abstract: Three-dimensional reaching by non-human primates is an important behavioral paradigm for investigating representations existing in motor control areas of the brain. Most studies to date have correlated neural activity to a few of the many arm motion parameters including: global hand position or velocity, joint angles, joint angular velocities, joint torques or muscle activations. So far, no single study has been able to incorporate all these parameters in a meaningful way that would allow separation of these often highly correlated variables. This paper introduces a three-dimensional, seven degree-of-freedom computational musculoskeletal model of the macaque arm that translates the coordinates of eight tracking markers placed on the arm into joint angles, joint torques, musculotendon lengths and finally into an optimized prediction of muscle forces. This paper uses this model to illustrate how the classic center-out reaching task used by many researchers over the last 20 years is not optimal in separating out intrinsic, extrinsic, kinematic and kinetic variables. However, by using the musculoskeletal model to design and test novel behavioral movement tasks, a priori, it is possible to disassociate the myriad of movement parameters in motor neurophysiological reaching studies.

Bladder emptying by intermittent electrical stimulation of the pudendal nerve

Abstract: Persons with a suprasacral spinal cord injury cannot empty their bladder voluntarily. Bladder emptying can be restored by intermittent electrical stimulation of the sacral nerve roots (SR) to cause bladder contraction. However, this therapy requires sensory nerve transection to prevent dyssynergic contraction of the external urethral sphincter (EUS). Stimulation of the compound pudendal nerve trunk (PN) activates spinal micturition circuitry, leading to a reflex bladder contraction without a reflex EUS contraction. The present study determined if PN stimulation could produce bladder emptying without nerve transection in cats anesthetized with alpha-chloralose. With all nerves intact, intermittent PN stimulation emptied the bladder (64 +/- 14% of initial volume, n = 37 across six cats) more effectively than either distention-evoked micturition (40 +/- 19%, p 0.10), indicating that PN stimulation was not limited by bladder-sphincter dyssynergia. Intermittent PN stimulation holds promise for restoring bladder emptying following spinal injury without requiring nerve transection.

Design, in vitro and in vivo assessment of a multi-channel sieve electrode with integrated multiplexer.

Abstract: This paper reports on the design, in vitro and in vivo investigation of a flexible, lightweight, polyimide based implantable sieve electrode with a hybrid assembly of multiplexers and polymer encapsulation. The integration of multiplexers enables us to connect a large number of electrodes on the sieve using few input connections. The implant assembly of the sieve electrode with the electronic circuitry was verified by impedance measurement. The 27 platinum electrodes of the sieve were coated with platinum black to reduce the electrode impedance. The impedance magnitude of the electrode sites on the sieve (geometric surface area 2,200 microm(2)) was |Z(f=1kHz)| = 5.7 kOmega. The sieve electrodes, encased in silicone, have been implanted in the transected sciatic nerve of rats. Initial experiments showed that axons regenerated through the holes of the sieve and reinnervated distal target organs. Nerve signals were recorded in preliminary tests after 3-7 months post-implantation.

Different classification techniques considering brain computer interface applications

Abstract: In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.

Neuronal network structuring induces greater neuronal activity through enhanced astroglial development

Abstract: The confluence of micropatterning, microfabricated multielectrode arrays, and low-density neuronal culture techniques make possible the growth of patterned neuronal circuits overlying multielectrode arrays. Previous studies have shown synaptic interaction within patterned cultures which was more active on average than random cultures. In our present study, we found patterned cultures to have up to five times more astrocytes and three times more neurons than random cultures. In addition, faster development of synapses is also seen in patterned cultures. Together, this yielded greater overall neuronal activity as evaluated by the number of active electrodes. Our finding of astrocytic proliferation within serum-free culture is also novel.

Validating biorobotic models.

Abstract: Some issues in neuroscience can be addressed by building robot models of biological sensorimotor systems. What we can conclude from building models or simulations, however, is determined by a number of factors in addition to the central hypothesis we intend to test. These include the way in which the hypothesis is represented and implemented in simulation, how the simulation output is interpreted, how it is compared to the behaviour of the biological system, and the conditions under which it is tested. These issues will be illustrated by discussing a series of robot models of cricket phonotaxis behaviour.

Chronic histological effects of the flat interface nerve electrode.

Abstract: The flat interface nerve electrode (FINE) is designed to reshape peripheral nerves into favorable geometries for selective stimulation. Compared to cylindrical geometries, the ovoid geometries created by the FINE allow more space for contact placement. Furthermore, the amount of electrically excitable tissue between stimulating contacts and target axons is reduced. In this study, the nerve response to the presence of the FINE is examined histologically. Three different FINEs were designed to reshape peripheral nerves to different opening heights designated as 'wide' (1.3 mm), 'medium' (0.5 mm) and 'narrow' (0.1 mm) cuffs. Twelve adult cats were implanted with one cuff each (four in total of each design) on their right sciatic nerves. At least 3 months later, the animals were sacrificed and their sciatic nerves were harvested for histological evaluation. Cross-sectional areas and eccentricities (defined as the major axis divided by the minor axis of the closest fit ellipse to a region) of the nerves were measured to assess the degree of reshaping. The wide and medium cuff designs significantly reshaped the nerves compared to control nerves, though there was no significant difference in eccentricity between nerves implanted with wide and medium cuffs. There was extensive deposition of connective tissue in the epineurium of all nerves implanted with cuffs. No significant difference in fiber counts was measured in any of the groups studied. Only nerves implanted with narrow cuffs showed evidence of axonal injury and/or demyelination. These results, coupled with stimulation selectivity measurements made on the same animals, suggest that safe, selective electrodes can be designed with ovoid geometries. Moderate reshaping caused no damage, while extreme reshaping generated mild-to-moderate nerve damage. It might be possible, however, to redesign the cuffs to slowly reshape the nerves.

The present and future

Abstract: Neural engineering has grown substantially in the last few years and it is time to review the progress of the first journal in this field. Journal of Neural Engineering (JNE) is a quarterly publication that started in 2004. The journal is now in its third volume and eleven issues, consisting of 114 articles in total, have been published since its launch. The editorial processing times have been kept to a minimum, the receipt to first decision time is 41 days, on average, and the time from receipt to publication has been maintained below three months. It is also worth noting that it is free to publish in Journal of Neural Engineering—there are no author fees—and once published the articles are free online for the first month. The journal has been listed in Pubmed® since 2005 and has been accepted by ISI® in 2006. Who is reading Journal of Neural Engineering? The number of readers of JNE has increased significantly from 8050 full-text downloads in 2004 to 14 900 in 2005 and the first seven months of 2006 have already seen 12 800 downloads. The top users in 2005 were the Microsoft Corporation, Stanford University and the University of Michigan. The list of top ten users also includes non-US institutions: University of Toronto, University of Tokyo, Hong Kong Polytechnic, National Library of China and University College London, reflecting the international flavor of the journal. What are the hot topics in neural engineering? Based on the number of downloads and citations for 2004–2005, the top three topics are: (1) Brain–computer interfaces (2) Visual prostheses (3) Neural modelling Several other topics such as microelectrode arrays, neural signal processing, neural dynamics and neural circuit engineering are also in the top ten. Where are Journal of Neural Engineering articles cited? JNE articles have reached a wide audience and have been cited in of some of the best journals in physiology and neuroscience such as Nature Neuroscience, Journal of Neuroscience, Trends in Neuroscience, Journal of Physiology, Proceedings of the National Academy of Science as well as in engineering and physics journals such as Annals of Biomedical Engineering, Physical Review Letters and IEEE Transactions on Biomedical Engineering. However, the number of citations in clinical journals is limited. What is special about Journal of Neural Engineering? JNE has published two special issues: (1) The Eye and the Chip (visual prostheses) (vol. 2, (1), 2005) and (2) Sensory Integration: Role of Internal Models (vol. 2, (3), 2005). These special issues have attracted a lot of attention based on the number of article downloads. JNE also publishes tutorials intended to provide background information on specific topics such as classification, sensory substitution and cortical neural prosthetics. A series of tutorials from the 3rd Neuro-IT and Neuroengineering Summer School has been published with the first appearing in vol. 2 (4), 2005. What is in the future for Journal of Neural Engineering? The goal of any journal should be to provide a particular field with the best venue for scientists and engineers to make their work available and noticeable to the rest of the community. In particular, attracting a strong readership base and high quality manuscripts should be the first priority. Providing accurate, reliable and speedy reviews should be the next. With an international board of experts in the field of neural engineering, a solid base of reviewers, readers and contributors, JNE is in a strong position to continue to serve the neural engineering community. However, this is still a small community and growth is essential for continued success in this area. There are two areas of expansion of great interest for the field of neural engineering currently poised between basic science on one hand and clinical implementation on the other: translational neuroscience and therapeutic neural engineering. We should strive to bridge the gap between basic neuroscience, clinical science and engineering by attracting contributions from neuroscientists and clinicians with an interest in neural engineering. I urge members of the neural engineering community to encourage their colleagues in these areas to consider JNE for publication of those manuscripts at the interface with neuroscience and engineering. I would like to take this opportunity to acknowledge the work of the board members, the reviewers of the articles and the staff at the Institute of Physics Publishing for their contribution to the Journal of Neural Engineering.