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Movement-related potentials (MRP), a component of the electroencephalogram (EEG) generated during voluntary movements, are known to vary during adaptation to changing loads and to different load types. This study attempts to reveal these changes. A novel denoising algorithm based on iterative approximation was applied to the MRPs recorded from four subjects while performing simple movements against changing loads. The results show that when subjects perform a repetitive task under a constant load there appears a significant peak in the activity of several MRP components recorded over the prefrontal cortex during the third and fourth repetition of the task. Furthermore, different types of loads do not affect the shape of the MRP but different force intensities do.

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The information transmission properties of ensembles of MSs and the effect of the gamma system on these properties were studied. Three converging lines of research were taken: (1) the development of information theoretic estimation tools, and the formulation of an "operational" interpretation for the information rate; (2) animal experiments in which the mutual information rate was estimated and the effect of the gamma system was quantified; (3) simulation of a muscle spindle model with gamma activation in order to corroborate the results of the animal experiments. The main hypothesis was that the gamma system will enhance information theoretic measures that quantify the quality of the sensory neural channel comprised from an ensemble of primary muscle spindle afferents. A random stimulus was applied to a muscle in the hind limb of a cat, while spike trains from several primary MS afferents were recorded simultaneously. The stimulus was administered twice, with an operative and a disconnected gamma system. The mutual information rate between the stimulus and spike trains, as well as other information theoretic measures, was estimated. The information rate of ensembles of MSs increased with increasing ensemble size. However, with an operative gamma system the "ensemble effect" was much higher. In addition, the ensemble effect was influenced by the stimulus spectrum. A muscle spindle population model with gamma activation was simulated with stimuli that were identical to that of the animal experiments. The simulation results supported the experimental results and corroborated the main hypothesis. The results indicate that the gamma system has an important role in enhancing information transmission from ensembles of MSs to the spinal cord.

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This study aims to recover transient, trial-varying evoked potentials (EPs), in particular the movement-related potentials (MRPs), embedded within the background cerebral activity at very low signal-to-noise ratios (SNRs). A new adaptive neuro-fuzzy technique will attempt to estimate movement-related potentials within multi-channel EEG recordings, enabling this method to completely adapt to each input sweep without system training procedures. We assume that one of the sensors is corrupted by noise deriving from other sensors via an unknown function that will be estimated. We will approach this problem by: (1) spatially decorrelating the sensors in the preprocessing phase, (2) choosing the most informative of the filtered channels that will permit the best MRP estimation (input-selection phase) and (3) training the neuro-fuzzy model to fit the noise over the chosen sensor and therefore estimating the buried MRP. We tested this framework with simulations to validate the analytical results before applying them to the real biological data. Whenever it is applied to biological data, this method improves the SNR by more than 12 dB, even to very low SNRs. The processing method proposed here is likely to complement other estimation techniques and can be useful to process, enhance and analyse single-trial MRPs.

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Brain-computer interfaces are devices for enabling patients with severe motor disorders to communicate with the world. One method for operating such devices is to use movement-related potentials that are generated in the brain when the patient moves, or imagines a movement of, one of his limbs. An algorithm for detecting movement-related potentials using a small number of EEG channels was developed. This algorithm is a combination of the matched filter, a non-linear transformation previously developed as part of a similar detector, and a classifier. The algorithm was compared with previous designs of similar detectors by both theoretic analysis and off-line evaluation of performance on data recorded from five subjects. It is shown that the performance of the algorithm was superior to that of previous methods, improving the area under the receiver operating characteristic curve to 87.8%, an improvement of 25% compared with the best previously suggested detection method. Finally, the probable sources for false detections were identified, and possible ways to minimise them are proposed.

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The information transmission properties of single, de-efferented primary muscle-spindle afferents from the hind limb of the cat were investigated. The gastrocnemius medialis muscle was stretched randomly while recording spike trains from several muscle-spindle afferents in the dorsal root. Two classes of input stimuli were used: (i) Gaussian noise with band-limited flat spectrum, and (ii) Gaussian noise with a more "naturalistic" 1/f(n) spectrum. The "reconstruction" method was used to calculate a lower bound to the information rate (in bits per second) between the muscle spindles and the spinal cord. Results show that in response to the flat-spectrum input, primary muscle-spindle afferents transfer information mainly about high frequencies, carrying 2.12 bits/spike. In response to naturalistic-spectrum inputs, primary muscle-spindle afferents transfer information about both low and high frequencies, with "spiking efficiency" increasing to 2.67 bits/spike. A simple muscle-spindle simulation model was analyzed with the same method, emphasizing the important part played by the intrafusal fiber mechanical properties in information transmission.

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The information transmission properties of single, deefferented, primary muscle spindle afferents (MSAs) from the hind limb of the cat were investigated. Random stretches were delivered to the gastrocnemius medialis muscle, while recording spike trains from several MSAs near the dorsal root. Two classes of input stimuli were used: Gaussian noise with band-limited flat spectrum, and Gaussian noise with a more "naturalistic" 1/fn spectrum. The "reconstruction" method was used to calculate a lower bound to the information rate (in bit/ sec) delivered from MSAs to the spinal cord. Results show that in response to flat spectrum primary MSAs transfer information mainly about high frequencies, carrying 1.97 bits per spike. In response to naturalistic spectrum MSAs transfer information about both low and high frequencies, with "spiking efficiency" increasing to 2.99 bits per spike. A simple muscle spindle model was simulated, exemplifying the part of the intrafusal fiber mechanical properties in information transmission.

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Movement-related potentials (MRPs) recorded from the brain are thought to vary during learning of a motor task. However, since MRPs are recorded at a very low signal-to-noise ratio, it is difficult to measure these variations. In this study we attempt to remove most of the accompanying noise thus enabling the tracking of transient phenomena in MRPs recorded during learning of a motor task. Subjects performed a simple motor task which required learning. A modified version of the matching pursuit algorithm was used in order to remove a significant portion of the electroencephalographic noise overlapping the MRPs recorded in the experiment. Small groups of MRPs were then averaged according to experimental parameters. Our results show that the power of the MRPs does not decay uniformly during learning. Instead, there is a significant peak in their power after 4 or 5 repetitions of the task. This peak is noticeable especially in electrodes placed over the prefrontal region of the cortex at times subsequent to the actual movement. The observed pattern of activity may indicate problem solving related to comprehension of the force against which the user performed the task. It is possible that this problem solving occurs in the prefrontal cortex.

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Movement-related potentials (MRPs) recorded from the brain may be affected by several factors. These include the how well the subject knows the task and the load against which he performs it. The objective of this study is to determine how dominant these two factors are in influencing the shape and power of MRPs. MRPs were recorded during performance of a simple motor task that required learning of a force. A stochastic algorithm was used in order to partition a set of MRPs that are embedded in the surrounding electroencephalographic (EEG) activity into distinct classes according to the power of the underlying MRPs. Our results show that the most influential factor in the partition was the load against which the subject performed the task. Furthermore, it was found that learning has a smaller, though not insignificant, influence on the power of the MRPs.

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The construction of a feed-forward controller frequently requires the estimation of an inverse function. Two possible methods to achieve this are: (i) learning the best estimated inverse (BEI), a method that is sometimes referred to as direct inverse learning and (ii) learning the inverse of the best estimator (IBE), a method that is sometimes referred to as indirect inverse learning. We analyze a general control problem, in the presence of noise, and show analytically that these two methods are asymptotically significantly different, even for simple linear non-redundant systems. We further demonstrate that the IBE method is typically superior for control purposes.

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We present a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. Our method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-session variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.

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OBJECTIVES: A method by which potentials related to voluntary movement can be recorded noninvasively from the human spinal cord is presented. METHODS: A novel signal processing technique performed on signals recorded by surface electrodes placed over the spinal column was used to filter time-locked back muscle noise, so that the only remaining signals were the spinal movement-related potentials from the brain to the limbs and vice versa. RESULTS: The signals obtained from 7 subjects using this technique are shown and temporally compared with movement-related cortical potentials (MRCP) and muscle electromyogram. It is demonstrated that the spinal signal starts approximately 600 ms before the actual movement, and that some features of this signal correspond to changes in cortical potentials. CONCLUSIONS: These findings imply that the spinal cord is not a simple command-carrying medium from the brain to the limbs, and implies that some computational activities take place at the spinal cord level.

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Rapid human movements exhibit a quasilinear relationship between their amplitude and maximum velocity and a log-like relationship between their amplitude and duration. The authors demonstrate that those well-observed relations can be obtained with a simple nonlinear muscle model and a pulse-step control scheme. That result encourages the use of nonlinear musculoskeletal models with simple control schemes for modeling human ballistic movements.

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Reaching movement is a fast movement towards a given target. The main characteristics of such a movement are straight path and a bell-shaped speed profile. In this work a mathematical model for the control of the human arm during ballistic reaching movements is presented. The model of the arm contains a 2 degrees of freedom planar manipulator, and a Hill-type, non-linear mechanical model of six muscles. The arm model is taken from the literature with minor changes. The nervous system is modeled as an adjustable pattern generator that creates the control signals to the muscles. The control signals in this model are rectangular pulses activated at various amplitudes and timings, that are determined according to the given target. These amplitudes and timings are the parameters that should be related to each target and initial conditions in the work-space. The model of the nervous system consists of an artificial neural net that maps any given target to the parameter space of the pattern generator. In order to train this net, the nervous system model includes a sensitivity model that transforms the error from the arm end-point coordinates to the parameter coordinates. The error is assessed only at the termination of the movement from knowledge of the results. The role of the non-linearity in the muscle model and the performance of the learning scheme are analysed, illustrated in simulations and discussed. The results of the present study demonstrate the central nervous system's (CNS) ability to generate typical reaching movements with a simple feedforward controller that controls only the timing and amplitude of rectangular excitation pulses to the muscles and adjusts these parameters based on knowledge of the results. In this scheme, which is based on the adjustment of only a few parameters instead of the whole trajectory, the dimension of the control problem is reduced significantly. It is shown that the non-linear properties of the muscles are essential to achieve this simple control. This conclusion agrees with the general concept that motor control is the result of an interaction between the nervous system and the musculoskeletal dynamics.

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In this paper, we present a novel approach to solving the single-trial evoked-potential estimation problem. Recognizing that different components of an evoked potential complex may originate from different functional brain sites and can be distinguished according to their respective latencies and amplitudes, we propose an estimation approach based on identification of evoked potential components on a single-trial basis. The estimation process is performed in two stages: first, an average evoked potential is calculated and decomposed into a set of components, with each component serving as a subtemplate for the next stage; then, the single measurement is parametrically modeled by a superposition of an emulated ongoing electroencephalographic activity and a linear combination of latency and amplitude-corrected component templates. Once optimized, the model provides the two assumed signal contributions, namely the ongoing brain activity and the single evoked brain response. The estimator's performance is analyzed analytically and via simulation, verifying its capability to extract single components at low signal-to-noise ratios typical of evoked potential data. Finally, two applications are presented, demonstrating the improved analysis capabilities gained by using the proposed approach. The first application deals with movement related brain potentials, where a change of the single evoked response due to external loading is detected. The second application involves cognitive event-related brain potentials, where a dynamic change of two overlapping components throughout the experimental session is detected and tracked.

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Current estimators for single-trial evoked potentials (EP's) require a signal-to-noise ratio (SNR) of 0 dB or better to obtain high quality estimations, yet many types of EP's suffer from substantially lower SNR's. This paper presents a robust-evoked-potential-estimator (REPE) facilitating high quality estimations of single movement related EP's with a relatively low SNR. The estimator is based on a standard ARX model, enhanced to support estimation under poor SNR conditions. The REPE was tested successfully on a computer simulated data set giving reliable single-trial estimations for the low SNR range of around -20 dB. THe REPE was also applied to experimental data, producing clear single-trial estimations of movement related brain signals recorded in a classic scenario of self-paced finger tapping experiment.

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A fast segmentation-based Matched Filtering (MF) technique of single trial Evoked Potentials (EP's) is presented. MF improves the Signal-to-Noise Ratio of single EP's, reducing the number of repetitions necessary to obtain high quality signals by an order of magnitude. A computer simulation and analysis of experimental data of Movement Related Potentials and cognitive Event Related Potentials demonstrate the superior capabilities of MF compared to traditional Ensemble Averaging.

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Using the electrical impedance measurement technique to investigate stroke volume estimation, three models of the ventricle were simulated. A four-electrode impedance catheter was used; two electrodes to set up an electric field in the model and the other two to measure the potential difference. A new approach, itself an application of the quasi-static case of a method used to solve electromagnetic field problems, was used to solve the electric field in the model. The behaviour of the estimation is examined with respect to the electrode configuration on the catheter and to catheter location with respect to the ventricle walls. Cardiac stroke volume estimation was found to be robust to catheter location generating a 10 per cent error for an offset of 40 per cent of the catheter from the chamber axis and rotation of 20 degrees with respect to the axis. The electrode configuration has a dominant effect on the sensitivity and accuracy of the estimation. Certain configurations gave high accuracy, whereas in others high sensitivity was found with lower accuracy. This led to the conclusion that the electrode configuration should be carefully chosen according to the desired criteria.

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In closed-loop functional neuromuscular stimulation (FNS)-assisted paraplegic walking, there is a need for reference leg motion trajectories that describe the desired walking cycle. These reference trajectories were defined as the angular changes between the leg segments, as measured by an electrogoniometer system. For each leg, the hip, knee and ankle trajectories of normal individuals during slow walking were measured and sampled over a number of cycles. Additionally, foot contact with the ground was measured to synchronise the various walking trajectories. Each joint's angular trajectory was averaged over a number of walking cycles, using an interpolation method based on a discrete Fourier transform (DFT) and inverse DFT technique, to expand all the signals to the same length. In this way an average walking cycle was obtained for each trial, representing the six averaged leg motion trajectories for one walking cycle. Angular trajectories and walking parameters for slow and normal walking were compared so as to investigate principles of walking cycle adaptation necessary to stabilise the body during slow walking. In general, angular trajectories were similar for different subjects, but different for different walking speeds, due to the greater demands on maintaining stability during slow walking. It can be concluded that normal speed walking consists of separate, unstable phases, whereas slow speed walking, relevant for paraplegic walking, requires stabilising each separate phase of the walking cycle.

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How memorized visuo-spatial information influences motor control and whether this information is able to replace the feedback processing in cases of visual deprivation was studied using an unrestrained finger- and hand-movement paradigm. Nineteen right-handed subjects were asked to grasp and lift a small block with the index finger and thumb of the right hand, as quickly as possible. The efficiency of motor performance was analysed by measuring the grasping time derived from tangential velocity profiles of the fingertips. The data revealed significantly shorter grasping times under continuous visual guidance than during blind grasping. Grasping times increased under conditions with stepwise prolongation of visual deprivation time prior to the movement onset. The results support the general concept that within the first seconds of visual deprivation, stored visuo-spatial information can partly compensate for the lack of continuous visual feedback.

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