2008
DOI: 10.1016/j.jneumeth.2008.04.025
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On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes

Abstract: Among the possible interfaces with the peripheral nervous system (PNS), intraneural electrodes represent an interesting solution for their potential advantages such as the possibility of extracting spikes from electroneurographic (ENG) signals. Their use could increase the precision and the amount of information which can be detected with respect to other processing methods.In this study, in order to verify this assumption, thin-film longitudinal intrafascicular electrodes (tfLIFE) were implanted in the sciati… Show more

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Cited by 105 publications
(111 citation statements)
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“…Other techniques, more specific for PNS signals and based on spike sorting and classification, are more challenging form a computational perspective [7]. Taking into account the final goal of this work, in this paper one of these algorithms has been selected [11]. Originally tested on the animal model to decode afferences, i.e.…”
Section: Motivation and Backgroundmentioning
confidence: 99%
“…Other techniques, more specific for PNS signals and based on spike sorting and classification, are more challenging form a computational perspective [7]. Taking into account the final goal of this work, in this paper one of these algorithms has been selected [11]. Originally tested on the animal model to decode afferences, i.e.…”
Section: Motivation and Backgroundmentioning
confidence: 99%
“…More information can be found in [28]. Various sensory stimuli were applied to the hind limb (i.e., ankle flexion/extension, flexion/extension of one or more toes, and stroking of cutaneous receptive fields) and the elicited signals were recorded using the tfLIFEs.…”
Section: Protocol Of the Experiments For Classificationmentioning
confidence: 99%
“…For each animal, the recording site with best signal to noise ratio was chosen using the automated procedure described in [28].…”
Section: Protocol Of the Experiments For Classificationmentioning
confidence: 99%
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