title: A Spiking Neural Network in sEMG Feature Extraction.
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.
authors.affiliation: Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia. lobov@neuro.nnov.ru.
authors.forename: Sergey
authors.initials: S
authors.lastname: Lobov
authors.affiliation: Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia. mironov@neuro.nnov.ru.
authors.forename: Vasiliy
authors.initials: V
authors.lastname: Mironov
authors.affiliation: Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia. kastalskiy@neuro.nnov.ru.
authors.forename: Innokentiy
authors.initials: I
authors.lastname: Kastalskiy
authors.affiliation: Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia. kazantsev@neuro.nnov.ru.
authors.forename: Victor
authors.initials: V
authors.lastname: Kazantsev
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000328:Adult; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: artificial neural network; exoskeleton; feature extraction; neurointerface; pattern classification; sEMG