http://ieeexplore.ieee.org TOC Alert for Publication# 7333 2009 June 19 17 3 C1 C1 235 17 3 C2 C2 42 17 3 201 202 397 Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional connectivity (prior) can improve inference of these parameters and reduce overfitting. Recent studies have used likelihood functions based on the statistics of point-processes and a prior that captures the sparseness of neural connections. Here we include a prior that captures the empirical finding that interactions tend to vary smoothly in time. We show that this method can successfully infer connectivity patterns in simulated data and apply the algorithm to spike data recorded from primary motor (M1) and premotor (PMd) cortices of a monkey. Finally, we present a new approach to studying structure in inferred connections based on a Bayesian clustering algorithm. Groups of neurons in M1 and PMd show common patterns of input and output that may correspond to functional assemblies. ]]> 17 3 203 213 1166 While temporal lobe epilepsy (TLE) has been treatable with anti-seizure medications over the past century, there still remain a large percentage of patients whose seizures remain untreatable pharmacologically. To better understand and treat TLE, our laboratory uses several in vivo analytical techniques to estimate connectivity in epilepsy. This paper reviews two different connectivity-based approaches with an emphasis on application to the study of epilepsy. First, we present effective connectivity techniques, such as Granger causality, that has been used to assess the dynamic directional relationships among brain regions. These measures are used to better understand how seizure activity initiates, propagates, and terminates. Second, structural techniques, such as magnetic resonance imaging, can be used to assess changes in the underlying neural structures that result in seizure. This paper also includes in vivo epilepsy-centered examples of both effective and anatomical connectivity analysis. These analyses are performed on data collected in vivo from a spontaneously seizing animal model of TLE. Future work in vivo on epilepsy will no doubt benefit from a fusion of these different techniques. We conclude by discussing the interesting possibilities, implications, and challenges that a unified analysis would present. ]]> 17 3 214 223 1551 In this paper, different linear and nonlinear methodologies for the estimation of cortical connectivity from neuroelectric and hemodynamic measurements are reviewed and applied on common data set in order to highlight similarities and differences in the results. Different effective and functional connectivity methods were applied to motor and cognitive data sets, including structural equation modeling (SEM), directed transfer function (DTF), partial directed coherence (PDC), and direct directed transfer function (dDTF). Comparisons were made between the results in order to understand if, for a same dataset, effective and functional connectivity estimators can return the same cortical connectivity patterns. An application of a nonlinear method [phase synchronization index (PSI)] to similar executed and imagined movements was also reviewed. Connectivity patterns estimated with the use of the neuroelectric information and of the information from the multimodal integration of neuroelectric and hemodynamic data were also compared. Results suggests that the estimation of the cortical connectivity patterns performed with the linear methods (SEM, DTF, PDC, dDTF) or with the nonlinear method (PSI) on movement related potentials returned similar cortical networks. Differences in cortical connectivity were noted between the patterns estimated with the use of multimodal integration and those estimated by using only the neuroelectric data. ]]> 17 3 224 233 776 The latency estimation of cortico-muscular coherence (CMCoh) could provide valuable information, especially for the pathological study. However, the conduction time from the central cortical rhythm to peripheral oscillations has not been explored for stroke patients. In this study one recently proposed method, maximizing coherence, was applied into the coherence analysis to estimate the latency by which the extensor carpi radialis electromyographic signals lagged behind the electroencephalographic time series with seven subcortical stroke subjects. Significantly prolonged conduction time was found in affected sides compared with the unaffected sides. The interhemispheric spatial displacement was also calculated using electrodes projection optimization and spherical surface laplacian. The results showed that the CMCoh could help investigate the cerebral reorganization after stroke. ]]> 17 3 234 243 1534 Transfer entropy (${rm TE}$) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of ${rm TE}$ that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. We show the application of the improved ${rm TE}$ method to long (in the order of days; approximately a total of 600 h across all patients), continuous, intracranial electroencephalograms (EEG) recorded in two different medical centers from four patients with focal temporal lobe epilepsy (TLE) for localization of their foci. All patients underwent ablative surgery of their clinically assessed foci. Based on a surrogate statistical analysis of the ${rm TE}$ results, it is shown that the identified potential focal sites through the suggested analysis were in agreement with the clinically assessed sites of the epileptogenic focus in all patients analyzed. It is noteworthy that the analysis was conducted on the available whole-duration multielectrode EEG, that is, without any subjective prior selection of EEG segments or electrodes for analysis. The above, in conjunction with the use of surrogate data, make the results of this analysis robust. These findings suggest a critical role ${rm TE}$ may play in epilepsy research in general, and as a tool for robust localization of the epileptogenic focus/foci in patients with focal epilepsy in particular. ]]> 17 3 244 253 1204 Brain–machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories—those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future. ]]> 17 3 254 262 705 The potential benefits of functional electrically stimulated (FES) cycling for people with spinal cord injury (SCI) are limited by the power output (PO) attainable. To understand why PO and metabolic efficiency are low, it is helpful to distinguish the effect of the SCI from the effects of electrical stimulation. The purpose of this study was to determine the performance of electrically stimulated (ES) muscle under simpler conditions and in able-bodied people in order to answer two questions about the causes of the poor efficiency in FES cycling. Fifteen able-bodied subjects (26.6 years, six male) performed 5 min of intermittent isometric quadriceps contractions at 40% maximum voluntary contraction during both voluntary and ES activation. Subsequently, nine of them performed 5 min of ES intermittent concentric contractions at the same intensity. This intermittent quadriceps activation imitated the muscles' activity during FES cycling at 35 rpm. Metabolic measurements were recorded. Input power relative to the integral of torque produced ( ${rm W}/{rm Nm}cdot {rm s}$) was significantly higher during ES than voluntary isometric contractions. Efficiency of ES concentric contractions was $29.6 pm 2.9%$. Respiratory exchange ratio was high during ES (1.00-1.01) compared with voluntary (0.91) contractions. ES is less economic than voluntary exercise during isometric contractions, probably due to the greater activation of fast muscle fibres. However, during ES concentric contractions, efficiency is near to the expected values for the velocity chosen. Thus there are additional factors that affect the inefficiency observed during FES cycling. ]]> 17 3 263 269 156 Pattern recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation. ]]> 17 3 270 278 539 This study undertakes a simulation and validation experiment to provide guidelines regarding pointing device usage for quadriplegic individuals assisted by a newly developed integrated pointing device apparatus (IPDA). The simulation experiment involving 30 normal subjects whose upper limb movement was restricted by splints. Another 15 subjects with high level cervical spinal cord injury (SCI) were recruited for the validation study. All normal subjects employed six control modes for target-acquisition and drag-and-drop tasks using an IPDA to integrate common pointing devices. A previously designed software was used to evaluate the operational efficiency (OE), expressed as “able performance” (%AP), of the subjects. The experimental results indicated that the OE of normal subjects for controlling the pointing devices were dominated first by using the unilateral hand (69–100 %AP), then by using the wrist/hand (65–73 %AP), and finally by using either bilateral body parts or the combination of limb and chin (45–53 %AP). The OE for operating an orientation-rotated mouse using the dominant wrist/hand via IPDA in both tasks was equivalent to that for operating a trackball using the dominant hand. The experimental results obtained by subjects with SCI also demonstrated similar findings, although the OEs in each control mode were lower than in normal subjects. Results of this study provide valuable guidelines for selecting and integrating common pointing devices using IPDA for quadriplegic individuals. The priority for selecting which body part should control the pointing devices was as follows: unilateral hands, unilateral wrist/hands, and either bilateral body parts or a limb and chin/head/neck in combination. ]]> 17 3 279 286 427 This paper presents a new learning method for Gaussian mixture models (GMMs) to improve their generalization ability. A traditional maximum a posterior (MAP) parameter estimate is used to achieve regularization based on conjugate priors. Plus, a model order selection criterion is derived from Bayesian–Laplace approaches, using the conjugate priors to measure the uncertainty of the estimated parameters. As a result, the proposed learning method avoids the possibility of convergence toward the boundary of the parameter space, and is also capable of selecting the optimal order for a GMM with more enhanced stability than conventional methods using a flat prior. When applying the proposed learning method to construct a GMM classifier for electromyogram (EMG) pattern recognition, the proposed GMM classifier achieves a high generalization ability and outperforms conventional classifiers in terms of recognition accuracy. ]]> 17 3 287 297 557 The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a discrete accelerometer-based activity monitor. The continuous wavelet transform (CWT) with various mother wavelets were applied to accelerometry data from three randomly selected patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive, and mixed motor subtypes. A classification tree used the periods of overall movement as measured by the discrete accelerometer-based monitor as determining factors for which to classify these delirious patients. This data used to create the classification tree were based upon the minimum, maximum, standard deviation, and number of coefficient values, generated over a range of scales by the CWT. The classification tree was subsequently used to define the remaining motoric subtypes. The use of a classification system shows how delirium subtypes can be categorized in relation to overall motoric behavior. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behavior differ in electronically measured activity levels. ]]> 17 3 298 307 1262 17 3 308 308 270 17 3 C3 C3 28 17 3 C4 C4 39