{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:28:04Z","timestamp":1770917284172,"version":"3.50.1"},"reference-count":15,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,11,3]],"date-time":"2015-11-03T00:00:00Z","timestamp":1446508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education and Science of Russian Federation","award":["RFMEFI58114X0011"],"award-info":[{"award-number":["RFMEFI58114X0011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s151127894","type":"journal-article","created":{"date-parts":[[2015,11,3]],"date-time":"2015-11-03T11:17:07Z","timestamp":1446549427000},"page":"27894-27904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Spiking Neural Network in sEMG Feature Extraction"],"prefix":"10.3390","volume":"15","author":[{"given":"Sergey","family":"Lobov","sequence":"first","affiliation":[{"name":"Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasiliy","family":"Mironov","sequence":"additional","affiliation":[{"name":"Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6050-4356","authenticated-orcid":false,"given":"Innokentiy","family":"Kastalskiy","sequence":"additional","affiliation":[{"name":"Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"Kazantsev","sequence":"additional","affiliation":[{"name":"Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Roche, A.D., Rehbaum, H., Farina, D., and Aszmann, O.C. 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Principles of Neural Science, McGraw-Hill. [4th ed.]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/11\/27894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:51:21Z","timestamp":1760215881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/11\/27894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,3]]},"references-count":15,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2015,11]]}},"alternative-id":["s151127894"],"URL":"https:\/\/doi.org\/10.3390\/s151127894","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,11,3]]}}}