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Sensors 2015, 15(11), 27894-27904;

A Spiking Neural Network in sEMG Feature Extraction

Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 8 September 2015 / Revised: 16 October 2015 / Accepted: 27 October 2015 / Published: 3 November 2015
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
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We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control. View Full-Text
Keywords: sEMG; feature extraction; pattern classification; artificial neural network; neurointerface; exoskeleton sEMG; feature extraction; pattern classification; artificial neural network; neurointerface; exoskeleton

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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 (CC BY 4.0).

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Lobov, S.; Mironov, V.; Kastalskiy, I.; Kazantsev, V. A Spiking Neural Network in sEMG Feature Extraction. Sensors 2015, 15, 27894-27904.

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