IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Feb 2004
Clinical Trial Controlled Clinical TrialSliding mode closed-loop control of FES: controlling the shank movement.
Functional electrical stimulation (FES) enables restoration of movement in individuals with spinal cord injury. FES-based devices use electric current pulses to stimulate and excite the intact peripheral nerves. They produce muscle contractions, generate joint torques, and thus, joint movements. ⋯ Such a controller was designed based on a mathematical neuromuscular-skeletal model and is founded on a sliding mode control theory. The controller was used to control shank movement and was tested in computer simulations as well as in actual experiments on healthy and spinal cord injured subjects. It demonstrated good robustness, stability, and tracking performance properties.
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IEEE Trans Biomed Eng · Feb 2004
Comparative StudyPrediction of myelinated nerve fiber stimulation thresholds: limitations of linear models.
Computer models of neurons are used to simulate neural behavior, and are important tools for designing neural prostheses. Computation time remains an issue when simulating large numbers of neurons or applying models to real time applications. Warman et al. developed a method to predict excitation thresholds for axons using linear models and a predetermined critical voltage. ⋯ Linear models were limited as effective tools for single fiber threshold prediction because accuracy was dependent on the nonlinear and linear models used, and any parameter that affected the extracellular potential distribution. Threshold prediction could be improved by appropriately choosing the membrane conductance of the linear model, but determination of an optimal conductance was computationally expensive. Finally, although single fiber threshold prediction error was partially masked when considering the input-output (I/O) properties of populations of axons, relatively large errors still occurred in population I/O curves generated with linear models.
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IEEE Trans Biomed Eng · Feb 2004
Predicting auditory tone-in-noise detection performance: the effects of neural variability.
Collecting and analyzing psychophysical data is a fundamental mechanism for the study of auditory processing. However, because this approach relies on human listening experiments, it can be costly in terms of time and money spent gathering the data. The development of a theoretical, model-based procedure capable of accurately predicting psychophysical behavior could alleviate these issues by enabling researchers to rapidly evaluate hypotheses prior to conducting experiments. ⋯ In this paper, we investigate the possibility that neural variability, which results from the randomness inherent in auditory nerve fiber responses, may explain some of the previously observed discrepancies. In addition, we study the impact of combining information across nerve fibers and investigate several models of multiple-fiber signal processing. Our findings suggest that neural variability can account for much, but not all, of the discrepancy between theoretical and experimental data.
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IEEE Trans Biomed Eng · Feb 2004
Internodal myelinated segments: delay and RGC time-domain Green function model.
The myelinated axon can be modeled by means of a distributed RGC circuit. The Green function of this model allows for a generic formulation of the internodal segment response to any kind of stimulus, and accounts for the delay of the action potential associated with this segment. The RGC model accuracy is comparable to that of a more complex electromagnetic model, and predicted delay agrees with experimental measurements.