Due to the very poor signal-to-noise ratios (SNR's) usually encountered with whole nerve-cuff signals, the processing method typically applied, rectification and windowed (bin)integration (RBI), can have serious shortcomings in extracting reliable information. In order to improve detection accuracy, these signals were further analyzed using statistical signal detection algorithms based on their second and higher order spectra (HOS). A comparison with both analog and digital RBI processing suggests that the statistical methods, due to their ability to separate the signal and noise subspaces, are superior. It was determined that the noise typically encountered with nerve-cuff electrode signals is normally (Gaussian) distributed. Therefore, third-order statistics can be applied to, ideally, completely reject the noise component. When cutaneous nerve recordings from the calcaneal nerve (innervating the heel area) were used in a dropfoot correction neural prosthesis, the detection percentage and the insensitivity to algorithm parameters were increased through the use of these statistical methods as to warrant their real-time implementation, and the inherent additional processing hardware that entails.
We have attempted to quantify the performance of natural versus artificial sensors when used in a closed-loop functional electrical stimulation system. Peroneal nerve stimulation was applied during gait to a multiple sclerosis subject with a drop foot. Stimulation was applied only during the swing phase to provide artificially induced dorsiflexion of the foot. Detection of the onset of the stance phase was accomplished using a standard heel contact switch mounted inside the subject's shoe (the artificial sensor) and using processed nerve signals derived from an implanted nerve-cuff electrode (the natural sensor). A detection percentage of at least 85% was achieved using the afferent nerve signal information only. When muscle activity (also recorded in the cuff) and additional information about the gait cycle were incorporated, functional detection ratios approaching 100% were achieved.
Human, afferent, whole nerve signals recorded using an implanted nerve-cuff electrode were analyzed using two algorithms based on the statistical properties of the signals. The processing method typically described in the literature (RectiJication and BinIntegration -RBI) has serious shortcomings in processing these signals, which have very poor signal-to-noise ratios. Algorithms based on a Singular Value Decomposition @4l) of the signal's 2nd and Higher-Order Statistics @OS) have resulted in more robust signal detection. Reliable detection of afferent nerve signals is essential i f such signals are to be of use in artificial sensoy-based Functional Electn'cal Stimulation neuralprosthetics.
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