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Sensitivity of a Frequency-Selective Electrode based on Spatial Spectral Properties of the Extracellular AP of Myelinated Nerve Fibers

Overview of attention for article published in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), January 2011
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Article details
Title
Sensitivity of a Frequency-Selective Electrode based on Spatial Spectral Properties of the Extracellular AP of Myelinated Nerve Fibers
Published in
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), January 2011
DOI 10.1109/iembs.2011.6091445
Pubmed ID
Authors
Abstract

In the context of functional electrical stimulation, neural recording is one of the main issues. For instance, the control of the limbs in people with motor deficiencies needs information about the muscle lengths and speeds that can be extracted from electroneurograms (ENG) carried on afferent peripheral nerves. The aim of this study is to propose an non-invasive and spatial-selective electrode (because specific informations are carried into different fascicles). To do so, we investigate the spatial properties of an extracellular action potential (AP). This properties are described qualitatively and quantitatively using analytical study on an inhomogeneous an anisotropic nerve model. Then, a spectral analysis on this spatial signal discriminates the different frequency components. Low spatial frequencies represent the global shape of the signal, whereas high frequencies are related to the type of fibers. We show that the latter is rapidly attenuated with the distance and thus, being a local phenomenon, can be used as a selective measurement. Finally, we propose a spatial filtering based on electrode design and an electronic architecture to extract this high frequencies.

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Geographical breakdown

Geographical breakdown
Country Count As %
China 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 7 33%
Student > Doctoral Student 2 10%
Student > Master 2 10%
Professor > Associate Professor 2 10%
Student > Postgraduate 2 10%
Other 3 14%
Unknown 3 14%
Readers by discipline
Readers by discipline Count As %
Engineering 9 43%
Computer Science 3 14%
Medicine and Dentistry 2 10%
Mathematics 1 5%
Nursing and Health Professions 1 5%
Other 1 5%
Unknown 4 19%