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Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded With Thin-Film Intrafascicular Electrodes

Milan Djilas 1, * Christine Azevedo Coste 1 David Guiraud 1 Kenichi Yoshida 2, 3
* Corresponding author
1 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00447972
Contributor : Christine Azevedo Coste <>
Submitted on : Tuesday, May 25, 2021 - 11:09:27 AM
Last modification on : Wednesday, May 26, 2021 - 12:49:48 PM
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Milan Djilas, Christine Azevedo Coste, David Guiraud, Kenichi Yoshida. Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded With Thin-Film Intrafascicular Electrodes. Computational Intelligence and Neuroscience, Hindawi Publishing Corporation, 2010, Special Issue "Signal Processing for Neural Spike Trains", pp.#836346. ⟨10.1155/2010/836346⟩. ⟨lirmm-00447972⟩

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