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Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia





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Academic Editors: Steffen Leonhardt and Daniel Teichmann

Abstract 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





Author: Sergey Lobov * , Vasiliy Mironov, Innokentiy Kastalskiy and Victor Kazantsev

Source: http://mdpi.com/



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