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Computational Intelligence and Neuroscience
Volume 2010, Article ID 836346, 13 pages
Research Article

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

1Vision Institute, 17 rue Moreau, 75012 Paris, France
2LIRMM/INRIA, University of Montpellier 2, 161 Rue Ada, 34095 Montpellier Cedex 5, France
3Biomedical Engineering Department, Indiana University-Purdue University Indianapolis, 723 W. Michigan St - SL220F, Indianapolis, IN 46202, USA
4Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7 D3, DK-9220 Aalborg, Denmark

Received 28 February 2009; Revised 5 December 2009; Accepted 15 January 2010

Academic Editor: Nitish Thakor

Copyright © 2010 Milan Djilas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.