Showing papers in "Journal of Neural Engineering in 2008"

Two-dimensional movement control using electrocorticographic signals in humans

Abstract: We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enable humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia.

Abstract: Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding.

Control of a humanoid robot by a noninvasive brain–computer interface in humans

Abstract: We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.

Transcranial current stimulation focality using disc and ring electrode configurations: FEM analysis.

Abstract: We calculated the electric fields induced in the brain during transcranial current stimulation (TCS) using a finite-element concentric spheres human head model. A range of disc electrode configurations were simulated: (1) distant-bipolar; (2) adjacent-bipolar; (3) tripolar; and three ring designs, (4) belt, (5) concentric ring, and (6) double concentric ring. We compared the focality of each configuration targeting cortical structures oriented normal to the surface ('surface-radial' and 'cross-section radial'), cortical structures oriented along the brain surface ('surface-tangential' and 'cross-section tangential') and non-oriented cortical surface structures ('surface-magnitude' and 'cross-section magnitude'). For surface-radial fields, we further considered the 'polarity' of modulation (e.g. superficial cortical neuron soma hyper/depolarizing). The distant-bipolar configuration, which is comparable with commonly used TCS protocols, resulted in diffuse (un-focal) modulation with bi-directional radial modulation under each electrode and tangential modulation between electrodes. Increasing the proximity of the two electrodes (adjacent-bipolar electrode configuration) increased focality, at the cost of more surface current. At similar electrode distances, the tripolar-electrodes configuration produced comparable peak focality, but reduced radial bi-directionality. The concentric-ring configuration resulted in the highest spatial focality and uni-directional radial modulation, at the expense of increased total surface current. Changing ring dimensions, or use of two concentric rings, allow titration of this balance. The concentric-ring design may thus provide an optimized configuration for targeted modulation of superficial cortical neurons.

Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation.

Abstract: In models of electrical stimulation of the nervous system, the electric potential is typically calculated using the quasi-static approximation. The quasi-static approximation allows Maxwell's equations to be simplified by ignoring capacitive, inductive and wave propagation contributions to the potential. While this simplification has been validated for bioelectric sources, its application to rapid stimulation pulses, which contain more high-frequency power, may not be appropriate. We compared the potentials calculated using the quasi-static approximation with those calculated from the exact solution to the inhomogeneous Helmholtz equation. The mean absolute errors between the two potential calculations were limited to 5–13% for pulse widths commonly used for neural stimulation (25 µs-1 ms). We also quantified the excitation properties of extracellular point source stimulation of a myelinated nerve fiber model using potentials calculated from each method. Deviations between the strength–duration curves for potentials calculated using the quasi-static (σ = 0.105 S m−1) and Helmholtz approaches ranged from 3 to 16%, with the minimal error occurring for 100 µs pulses. Differences in the threshold–distance curves for the two calculations ranged from 0 to 9%, for the same value of quasi-static conductivity. A sensitivity analysis of the material parameters revealed that the potential was much more strongly dependent on the conductivity than on the permittivity. These results indicate that for commonly used stimulus pulse parameters, the exact solution for the potential can be approximated by quasi-static simplifications only for appropriate values of conductivity.

Emulation of computer mouse control with a noninvasive brain-computer interface.

Abstract: Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.

Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.

Abstract: People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain–computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.

A high performance sensorimotor beta rhythm-based brain–computer interface associated with human natural motor behavior

Abstract: To explore the reliability of a high performance brain–computer interface (BCI) using non-invasive EEG signals associated with human natural motor behavior does not require extensive training. We propose a new BCI method, where users perform either sustaining or stopping a motor task with time locking to a predefined time window. Nine healthy volunteers, one stroke survivor with right-sided hemiparesis and one patient with amyotrophic lateral sclerosis (ALS) participated in this study. Subjects did not receive BCI training before participating in this study. We investigated tasks of both physical movement and motor imagery. The surface Laplacian derivation was used for enhancing EEG spatial resolution. A model-free threshold setting method was used for the classification of motor intentions. The performance of the proposed BCI was validated by an online sequential binary-cursor-control game for two-dimensional cursor movement. Event-related desynchronization and synchronization were observed when subjects sustained or stopped either motor execution or motor imagery. Feature analysis showed that EEG beta band activity over sensorimotor area provided the largest discrimination. With simple model-free classification of beta band EEG activity from a single electrode (with surface Laplacian derivation), the online classifications of the EEG activity with motor execution/motor imagery were: >90%/∼80% for six healthy volunteers, >80%/∼80% for the stroke patient and ∼90%/∼80% for the ALS patient. The EEG activities of the other three healthy volunteers were not classifiable. The sensorimotor beta rhythm of EEG associated with human natural motor behavior can be used for a reliable and high performance BCI for both healthy subjects and patients with neurological disorders. Significance: The proposed new non-invasive BCI method highlights a practical BCI for clinical applications, where the user does not require extensive training. M This article features online multimedia enhancements (Some figures in this article are in colour only in the electronic version)

A brain?computer interface using motion-onset visual evoked potential

Abstract: This paper presents a novel brain-computer interface (BCI) based on motion-onset visual evoked potentials (mVEPs). mVEP has never been used in BCI research, but has been widely studied in basic research. For the BCI application, the brief motion of objects embedded into onscreen virtual buttons is used to evoke mVEP that is time locked to the onset of motion. EEG data registered from 15 subjects are used to investigate the spatio-temporal pattern of mVEP in this paradigm. N2 and P2 components, with distinct temporo-occipital and parietal topography, respectively, are selected as the salient features of the brain response to the attended target that the subject selects by gazing at it. The computer determines the attended target by finding which button elicited prominent N2/P2 components. Besides a simple feature extraction of N2/P2 area calculation, the stepwise linear discriminant analysis is adopted to assess the target detection accuracy of a five-class BCI. A mean accuracy of 98% is achieved when ten trials data are averaged. Even with only three trials, the accuracy remains above 90%, suggesting that the proposed mVEP-based BCI could achieve a high information transfer rate in online implementation.

The Statistics of a Practical Seizure Warning System

Abstract: Statistical methods for evaluating seizure prediction algorithms are controversial and a primary barrier to realizing clinical applications. Experts agree that these algorithms must, at a minimum, perform better than chance, but the proper method for comparing to chance is in debate. We derive a statistical framework for this comparison, the expected performance of a chance predictor according to a predefined scoring rule, which is in turn used as the control in a hypothesis test. We verify the expected performance of chance prediction using Monte Carlo simulations that generate random, simulated seizure warnings of variable duration. We propose a new test metric, the difference between algorithm and chance sensitivities given a constraint on proportion of time spent in warning, and use a simple spectral power-based measure to demonstrate the utility of the metric in four patients undergoing intracranial EEG monitoring during evaluation for epilepsy surgery. The methods are broadly applicable to other scoring rules. We present them as an advance in the statistical evaluation of a practical seizure advisory system.

Three-dimensional neural constructs: a novel platform for neurophysiological investigation.

Abstract: Morphological and electrophysiological properties of neural cells are substantially influenced by their immediate extracellular surroundings, yet the features of this environment are difficult to mimic in vitro. Therefore, there is a tremendous need to develop a new generation of culture systems that more closely model the complexity of nervous tissue. To this end, we engineered novel electrophysiologically active 3D neural constructs composed of neurons and astrocytes within a bioactive extracellular matrix-based scaffold. Neurons within these constructs exhibited extensive 3D neurite outgrowth, expressed mature neuron-specific cytoskeletal proteins, and remained viable for several weeks. Moreover, neurons assumed complex 3D morphologies with rich neurite arborization and clear indications of network connectivity, including synaptic junctures. Furthermore, we modified whole-cell patch clamp techniques to permit electrophysiological probing of neurons deep within the 3D constructs, revealing that these neurons displayed both spontaneous and evoked electrophysiological action potentials and exhibited functional synapse formation and network properties. This is the first report of individual patch clamp recordings of neurons deep within 3D scaffolds. These tissue engineered cellular constructs provide an innovative platform for neurobiological and electrophysiological investigations, serving as an important step towards the development of more physiologically relevant neural tissue models.

Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task

Abstract: We developed an adaptive training algorithm, whereby an in vitro neocortical network learned to modulate its dynamics and achieve pre-determined activity states within tens of minutes through the application of patterned training stimuli using a multi-electrode array. A priori knowledge of functional connectivity was not necessary. Instead, effective training sequences were continuously discovered and refined based on real-time feedback of performance. The short-term neural dynamics in response to training became engraved in the network, requiring progressively fewer training stimuli to achieve successful behavior in a movement task. After 2 h of training, plasticity remained significantly greater than the baseline for 80 min (p-value<0.01). Interestingly, a given sequence of effective training stimuli did not induce significant plasticity (p-value=0.82) or desired behavior, when replayed to the network and no longer contingent on feedback. Our results encourage an in vivo investigation of how targeted multi-site artificial stimulation of the brain, contingent on the activity of the body or even of the brain itself could treat neurological disorders by gradually shaping functional connectivity.

Developing a tissue-engineered neural-electrical relay using encapsulated neuronal constructs on conducting polymer fibers

Abstract: Neural-electrical interface platforms are being developed to extracellularly monitor neuronal population activity. Polyaniline-based electrically conducting polymer fibers are attractive substrates for sustained functional interfaces with neurons due to their flexibility, tailored geometry and controlled electro-conductive properties. In this study, we addressed the neurobiological considerations of utilizing small diameter ( 85%) and intimate adhesion to PA–PP fibers. These efforts accomplished key prerequisites for the establishment of functional electrical interfaces with neuronal populations using small diameter PA–PP fibers—specifically, improved neurocompatibility, high-density neuronal adhesion and neuritic network development directly on fiber surfaces.

Kalman filter control of a model of spatiotemporal cortical dynamics

Abstract: Recent advances in Kalman filtering to estimate system state and parameters in nonlinear systems have offered the potential to apply such approaches to spatiotemporal nonlinear systems. We here adapt the nonlinear method of unscented Kalman filtering to observe the state and estimate parameters in a computational spatiotemporal excitable system that serves as a model for cerebral cortex. We demonstrate the ability to track spiral wave dynamics, and to use an observer system to calculate control signals delivered through applied electrical fields. We demonstrate how this strategy can control the frequency of such a system, or quench the wave patterns, while minimizing the energy required for such results. These findings are readily testable in experimental applications, and have the potential to be applied to the treatment of human disease.

Incorporation of the electrode electrolyte interface into finite-element models of metal microelectrodes

Abstract: An accurate description of the electrode–electrolyte interfacial impedance is critical to the development of computational models of neural recording and stimulation that aim to improve understanding of neuro–electric interfaces and to expedite electrode design. This work examines the effect that the electrode–electrolyte interfacial impedance has upon the solutions generated from time-harmonic finite-element models of cone- and disk-shaped platinum microelectrodes submerged in physiological saline. A thin-layer approximation is utilized to incorporate a platinum–saline interfacial impedance into the finite-element models. This approximation is easy to implement and is not computationally costly. Using an iterative nonlinear solver, solutions were obtained for systems in which the electrode was driven at ac potentials with amplitudes from 10 mV to 500 mV and frequencies from 100 Hz to 100 kHz. The results of these simulations indicate that, under certain conditions, incorporation of the interface may strongly affect the solutions obtained. This effect, however, is dependent upon the amplitude of the driving potential and, to a lesser extent, its frequency. The solutions are most strongly affected at low amplitudes where the impedance of the interface is large. Here, the current density distribution that is calculated from models incorporating the interface is much more uniform than the current density distribution generated by models that neglect the interface. At higher potential amplitudes, however, the impedance of the interface decreases, and its effect on the solutions obtained is attenuated.

Agarose and methylcellulose hydrogel blends for nerve regeneration applications.

Abstract: Trauma sustained to the central nervous system is a debilitating problem for thousands of people worldwide. Neuronal regeneration within the central nervous system is hindered by several factors, making a multi-faceted approach necessary. Two factors contributing to injury are the irregular geometry of injured sites and the absence of tissue to hold potential nerve guides and drug therapies. Biocompatible hydrogels, injectable at room temperature, that rapidly solidify at physiological temperatures (37 ◦ C) are beneficial materials that could hold nerve guidance channels in place and be loaded with therapeutic agents to aid wound healing. Our studies have shown that thermoreversible methylcellulose can be combined with agarose to create hydrogel blends that accommodate these properties. Three separate novel hydrogel blends were created by mixing methylcellulose with one of the three different agaroses. Gelation time tests show that the blends solidify at a faster rate than base methylcellulose at 37 ◦ C. Rheological data showed that the elastic modulus of the hydrogel blends rapidly increases at 37 ◦ C. Culturing experiments reveal that the morphology of dissociated dorsal root ganglion neurons was not altered when the hydrogels were placed onto the cells. The different blends were further assessed using dissolution tests, pore size evaluations using scanning electron microscopy and measuring the force required for injection. This research demonstrates that blends of agarose and methylcellulose solidify much more quickly than plain methylcellulose, while solidifying at physiological temperatures where agarose cannot. These hydrogel blends, which solidify at physiological temperatures naturally, do not require ultraviolet light or synthetic chemical cross linkers to facilitate solidification. Thus, these hydrogel blends have potential use in delivering therapeutics and holding scaffolding in place within the nervous system.

A self-paced brain-computer interface system with a low false positive rate.

Abstract: The performance of current EEG-based self-paced brain–computer interface (SBCI) systems is not suitable for most practical applications In this paper, an improved SBCI that uses features extracted from three neurological phenomena (movement-related potentials, changes in the power of Mu rhythms and changes in the power of Beta rhythms) to detect an intentional control command in noisy EEG signals is proposed The proposed system achieves a high true positive (TP) to false positive (FP) ratio To extract features for each neurological phenomenon in every EEG signal, a method that consists of a stationary wavelet transform followed by matched filtering is developed For each neurological phenomenon in every EEG channel, features are classified using a support vector machine classifier (SVM) For each neurological phenomenon, a multiple classifier system (MCS) then combines the outputs of the SVMs Another MCS combines the outputs of MCSs designed for the three neurological phenomena Various configurations for combining the outputs of these MCSs are considered A hybrid genetic algorithm (HGA) is proposed to simultaneously select the features, the values of the classifiers' parameters and the configuration for combining MCSs that yield the near optimal performance Analysis of the data recorded from four able-bodied subjects shows a significant performance improvement over previous SBCIs

A comparative study of three techniques for diameter selective fiber activation in the vagal nerve: anodal block, depolarizing prepulses and slowly rising pulses.

Abstract: The paper shows selective smaller fiber activation in the left and right vagal nerve in in vivo experiments in pigs using three different techniques: anodal block, depolarizing prepulses and slowly rising pulses. All stimulation techniques were performed with the same experimental setup. The techniques have been compared in relation to maximum achievable suppression of nerve activity, maximum required current, maximum achievable stimulation frequency and the required charge per phase. Suppression of the largest fiber activity (expressed as a percentage of the maximum response) was 0?40% for anodal block, 10?25% for depolarizing prepulses and 40?50% for slowly rising pulses (duration up to 5 ms). Incomplete suppression of activation was mainly attributed to the large size of the vagal nerve (3.0?3.5 mA) which resulted in a large difference of the excitation thresholds of nerve fibers at different distances from the electrode, as well as a relatively short duration of slowly rising pulses. The technique of anodal block required the highest currents. The techniques of slowly rising pulses and anodal block required comparable charge per phase that was larger than for the technique of depolarizing prepulses. Depolarizing prepulses were an optimal choice regarding maximum required current and charge per phase but were very sensitive to small changes of the current amplitude. The other two techniques were more robust regarding small changes of stimulation parameters. The maximum stimulation frequency, using typical values of stimulation parameters, was 105 Hz for depolarizing prepulses, 30 Hz for anodal block and 28 Hz for slowly rising pulses. Only a technique of depolarizing prepulses had a charge per phase within the safe limits. For the other two techniques it would be necessary to optimize the shape of a stimulation pulse in order to reduce the charge per phase.

Computational modeling of epidural cortical stimulation.

Abstract: Epidural cortical stimulation (ECS) is a developing therapy to treat neurological disorders. However, it is not clear how the cortical anatomy or the polarity and position of the electrode affects current flow and neural activation in the cortex. We developed a 3D computational model simulating ECS over the precentral gyrus. With the electrode placed directly above the gyrus, about half of the stimulus current flowed through the crown of the gyrus while current density was low along the banks deep in the sulci. Beneath the electrode, neurons oriented perpendicular to the cortical surface were depolarized by anodic stimulation, and neurons oriented parallel to the boundary were depolarized by cathodic stimulation. Activation was localized to the crown of the gyrus, and neurons on the banks deep in the sulci were not polarized. During regulated voltage stimulation, the magnitude of the activating function was inversely proportional to the thickness of the CSF and dura. During regulated current stimulation, the activating function was not sensitive to the thickness of the dura but was slightly more sensitive than during regulated voltage stimulation to the thickness of the CSF. Varying the width of the gyrus and the position of the electrode altered the distribution of the activating function due to changes in the orientation of the neurons beneath the electrode. Bipolar stimulation, although often used in clinical practice, reduced spatial selectivity as well as selectivity for neuron orientation.

Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs.

Abstract: Due to the relative noise and artifact insensitivity, steady-state visual evoked potential (SSVEP) has been used increasingly in the study of a brain–computer interface (BCI). However, SSVEP is still influenced by the same frequency component in the spontaneous EEG, and it is meaningful to find a parameter that can avoid or decrease this influence to improve the transfer rate and the accuracy of the SSVEP-based BCI. In this work, with wavelet analysis, a new parameter named stability coefficient (SC) was defined to measure the stability of a frequency, and then the electrode with the highest stability was selected as the signal electrode for further analysis. After that, the SC method and the traditional power spectrum (PS) method were used comparatively to recognize the stimulus frequency from an analogous BCI data constructed from a real SSVEP data, and the results showed that the SC method is better for a short time window data.

Submyelin potassium accumulation may functionally block subsets of local axons during deep brain stimulation: a modeling study

Abstract: Deep brain stimulation has been used for over a decade to relieve the symptoms of Parkinson's disease, although its mechanism of action remains poorly understood. To better understand the direct effects of DBS on central neurons, a computational model of a myelinated axon has been constructed which includes the effects of K+ accumulation within the peri-axonal space. Using best estimates of anatomic and electrogenic model parameters for in vivo STN axons, the model predicts a functional block along the axon due to K+ accumulation in the submyelin space. The functional block occurs for a range of model parameters: high stimulation frequencies (>130 Hz); high extracellular K+ concentrations (>3 × 10−3 M); low maximum Na+/K+ ATPase current densities (<0.026 A m−2); low diffusion coefficients for K+ diffusion out of the submyelin space (<2.4 × 10−9 m2 s−1); small periaxonal space widths of the myelin attachment sections (<2.7 × 10−9 m) and perinodal/internodal sections (<8.4 × 10−9 m). These results suggest that therapeutic DBS of the STN likely results in a functional block for many STN axons, although a subset of STN axons may also be activated at the stimulating frequency.

Brain-Computer Symbiosis

Abstract: The theoretical groundwork of the 1930s and 1940s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This paper is about the theoretical and practical possibility that direct communication between the brain and the computer can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body's input and output capacities can be overcome using direct interaction with the brain, and it discusses the assumptions, possible limitations and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades.

Computationally Efficient Models of Neuromuscular Recruitment and Mechanics

Abstract: We have improved the stability and computational efficiency of a physiologically realistic, virtual muscle (VM 3.*) model (Cheng et al 2000 J. Neurosci. Methods 101 117–30) by a simpler structure of lumped fiber types and a novel recruitment algorithm. In the new version (VM 4.0), the mathematical equations are reformulated into state-space representation and structured into a CMEX S-function in SIMULINK. A continuous recruitment scheme approximates the discrete recruitment of slow and fast motor units under physiological conditions. This makes it possible to predict force output during smooth recruitment and derecruitment without having to simulate explicitly a large number of independently recruited units. We removed the intermediate state variable, effective length (Leff), which had been introduced to model the delayed length dependency of the activation–frequency relationship, but which had little effect and could introduce instability under physiological conditions of use. Both of these changes greatly reduce the number of state variables with little loss of accuracy compared to the original VM. The performance of VM 4.0 was validated by comparison with VM 3.1.5 for both single-muscle force production and a multi-joint task. The improved VM 4.0 model is more suitable for the analysis of neural control of movements and for design of prosthetic systems to restore lost or impaired motor functions. VM 4.0 is available via the internet and includes options to use the original VM model, which remains useful for detailed simulations of single motor unit behavior.

Improved bladder emptying in urinary retention by electrical stimulation of pudendal afferents.

Abstract: Urinary retention is the inability to empty the bladder completely, and may result from bladder hypocontractility, increases in outlet resistance or both. Chronic urinary retention can lead to several urological complications and is often refractory to pharmacologic, behavioral and surgical treatments. We sought to determine whether electrical stimulation of sensory fibers in the pudendal nerve could engage an augmenting reflex and thereby improve bladder emptying in an animal model of urinary retention. We measured the efficiency of bladder emptying with and without concomitant electrical stimulation of pudendal nerve afferents in urethane-anesthetized rats. Voiding efficiency (VE = voided volume/initial volume) was reduced from 72 +/- 7% to 29 +/- 7% following unilateral transection of the sensory branch of the pudendal nerve (UST) and from 70 +/- 5% to 18 +/- 4% following bilateral transection (BST). Unilateral electrical stimulation of the proximal transected sensory pudendal nerve during distention-evoked voiding contractions significantly improved VE. Low-intensity stimulation at frequencies of 1-50 Hz increased VE to 40-51% following UST and to 39-49% following BST, while high-intensity stimulation was ineffective at increasing VE. The increase in VE was mediated by increases in the duration of distention-evoked voiding bladder contractions, rather than increases in contraction amplitude. These results are consistent with an essential role for pudendal sensory feedback in efficient bladder emptying, and raise the possibility that electrical activation of pudendal nerve afferents may provide a new approach to restore efficient bladder emptying in persons with urinary retention.

Bipolar electrode selection for a motor imagery based brain–computer interface

Abstract: A motor imagery based brain-computer interface (BCI) provides a non-muscular communication channel that enables people with paralysis to control external devices using their motor imagination. Reducing the number of electrodes is critical to improving the portability and practicability of the BCI system. A novel method is proposed to reduce the number of electrodes to a total of four by finding the optimal positions of two bipolar electrodes. Independent component analysis (ICA) is applied to find the source components of mu and alpha rhythms, and optimal electrodes are chosen by comparing the projection weights of sources on each channel. The results of eight subjects demonstrate the better classification performance of the optimal layout compared with traditional layouts, and the stability of this optimal layout over a one week interval was further verified.

Novel nanofibrous spiral scaffolds for neural tissue engineering

Abstract: Due to several drawbacks associated with autografts and allografts, tissue-engineering approaches have been widely used to repair peripheral nerve injuries. Most of the traditional tissue-engineered scaffolds in use are either tubular (single or multi-lumen) or hydrogel-based cylindrical grafts, which provide limited surface area for cell attachment and regeneration. Here, we show a novel poly(lactide-co-glycotide) (PLGA) microsphere-based spiral scaffold design with a nanofibrous surface that has enhanced surface areas and possesses sufficient mechanical properties and porosities to support the nerve regeneration process. These scaffolds have an open architecture that goes evenly throughout the scaffolds hence leaving enough volume for media influx and deeper cell penetration into the scaffolds. The in vitro tests conducted using Schwann cells show that the nanofibrous spiral scaffolds promote higher cell attachment and proliferation when compared to contemporary tubular scaffolds or nanofiber-based tubular scaffolds. Also, the nanofiber coating on the surfaces enhances the surface area, mimics the extracellular matrix and provides unidirectional alignment of cells along its direction. Hence, we propose that these scaffolds could alleviate some drawbacks in current nerve grafts and could potentially be used in nerve regeneration.

Classification of burst and suppression in the neonatal electroencephalogram

Abstract: Fisher's linear discriminant (FLD), a feed-forward artificial neural network (ANN) and a support vector machine (SVM) were compared with respect to their ability to distinguish bursts from suppressions in electroencephalograms (EEG) displaying a burst-suppression pattern Five features extracted from the EEG were used as inputs The study was based on EEG signals from six full-term infants who had suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer The results are summarized as the area under the curve (AUC), derived from receiver operating characteristic (ROC) curves for the three methods Based on this, the SVM performs slightly better than the others Testing the three methods with combinations of increasing numbers of the five features shows that the SVM handles the increasing amount of information better than the other methods

Selective extracellular stimulation of individual neurons in ganglia

Abstract: Selective control of individual neurons could clarify neural functions and aid disease treatments. To target specific neurons, it may be useful to focus on ganglionic neuron clusters, which are found in the peripheral nervous system in vertebrates. Because neuron cell bodies are found primarily near the surface of invertebrate ganglia, and often found near the surface of vertebrate ganglia, we developed a technique for controlling individual neurons extracellularly using the buccal ganglia of the marine mollusc Aplysia californica as a model system. We experimentally demonstrated that anodic currents can selectively activate an individual neuron and cathodic currents can selectively inhibit an individual neuron using this technique. To define spatial specificity, we studied the minimum currents required for stimulation, and to define temporal specificity, we controlled firing frequencies up to 45 Hz. To understand the mechanisms of spatial and temporal specificity, we created models using the NEURON software package. To broadly predict the spatial specificity of arbitrary neurons in any ganglion sharing similar geometry, we created a steady-state analytical model. A NEURON model based on cat spinal motor neurons showed responses to extracellular stimulation qualitatively similar to those of the Aplysia NEURON model, suggesting that this technique could be widely applicable to vertebrate and human peripheral ganglia having similar geometry.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy.

Abstract: The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Study on connectivity between coherent central rhythm and electromyographic activities.

Abstract: Whether afferent feedback contributes to the generation of cortico-muscular coherence (CMCoh) remains an open question. In the present study, a multivariate autoregressive (MVAR) model and partial directed coherence (PDC) were applied to investigate the causal influences between the central rhythm and electromyographic (EMG) signals in the process of CMCoh. The system modeling included activities from the contralateral and ipsilateral primary sensorimotor cortex (M1/S1), supplementary motor area (SMA) and the time series from extensor carpi radialis (ECR) muscles. The results showed that afferent sensory feedback could also play an important role for the generation of CMCoh. Meanwhile, significant coherence between the EMG signals and the activities in the SMA was found in two subjects out of five. Connectivity analysis revealed a significant descending information flow which possibly reflected direct recruitment on the motoneurons from the SMA to facilitate motor control.