Improving the signal-to-noise ratio on recordings with thin-film longitudinal intrafascicular electrodes using shielding cuffs

Milan Djilas 1 Kenichi Yoshida 2 Mathijs Kurstjens 2 Christine Azevedo Coste 1
1 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : An elegant solution to the problem of instrumenting paralyzed limbs with artificial sensors for use with closedloop FES systems is to use natural sensors, such as muscle afferent activity as feedback for the artificial controller. Longitudinal intra-fascicular electrodes (LIFEs) are electrodes that have shown promise in this application. As a peripheral nerve interface, they are designed to be placed inside the peripheral nerve, but near potentially active muscles and the stimulating electrode. Artefacts from EMG and stimulation remain limiting factors in signal acquisition. Here we present a technique for improving the signal-to-noise ratio which consists of wrapping a shield around the implant site of the recording electrode. Preliminary results obtained during in-vivo experiments suggest that the shielding increases the level of the neural signal in the recordings.
Type de document :
Communication dans un congrès
NER: Neural Engineering, May 2007, Kohala Coast, Hawaii, United States. 3rd IEEE EMBS Special Topic Conference on Neural Engineering, 2007, 〈http://ne2007.engineering.asu.edu〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00165457
Contributeur : Christine Azevedo Coste <>
Soumis le : jeudi 26 juillet 2007 - 11:25:33
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00165457, version 1

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Milan Djilas, Kenichi Yoshida, Mathijs Kurstjens, Christine Azevedo Coste. Improving the signal-to-noise ratio on recordings with thin-film longitudinal intrafascicular electrodes using shielding cuffs. NER: Neural Engineering, May 2007, Kohala Coast, Hawaii, United States. 3rd IEEE EMBS Special Topic Conference on Neural Engineering, 2007, 〈http://ne2007.engineering.asu.edu〉. 〈lirmm-00165457〉

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