{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T19:25:07Z","timestamp":1781724307374,"version":"3.54.5"},"reference-count":40,"publisher":"MIT Press - Journals","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:p> Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN. <\/jats:p>","DOI":"10.1162\/neco_a_01270","type":"journal-article","created":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T21:56:01Z","timestamp":1582062961000},"page":"741-758","source":"Crossref","is-referenced-by-count":6,"title":["Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network"],"prefix":"10.1162","volume":"32","author":[{"given":"Xugang","family":"Xi","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed M.","family":"Miran","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Center, George Washington University, Washington, DC, 20052, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Hua","sequence":"additional","affiliation":[{"name":"Jinhua People's Hospital, Jinhua, 321000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun-Bo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhizeng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2011.1054"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8513436"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.26417\/ejis.v2i3-35-44"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2004.10.001"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1109\/ICMA.2018.8484507"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-008-9219-8"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2012.2226905"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.08.020"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252500"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1063\/1.4746765"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"B12","first-page":"1013","volume-title":"Proceedings of the International Conference on NeuroRehabilitation","author":"Disselhorst-Klug C.","year":"2018"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2004.828048"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"B15","volume-title":"Deep learning","author":"Goodfellow I.","year":"2016"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1109\/CIACT.2018.8480172"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.07.004"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2870413"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206049"},{"key":"B20","first-page":"609","volume-title":"Advances in neural information processing systems, 15","author":"Jaeger H.","year":"2003"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1126\/science.1091277"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2007.04.016"},{"key":"B23","volume-title":"Principal component analysis","author":"Jolliffe I.","year":"2011"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.002"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2188414"},{"key":"B26","first-page":"1","volume":"31","author":"Li G.","year":"2018","journal-title":"Neural Computing and Applications"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2014.2302794"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1140\/epjst\/e2018-800001-8"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2018.2796070"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/s16081304"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1109\/LSENS.2019.2906386"},{"key":"B32","first-page":"138","volume-title":"Proceedings of the International Conference on Cognitive Systems and Signal Processing","author":"Peng F.","year":"2018"},{"key":"B33","first-page":"71","volume":"1","author":"Phinyomark A.","year":"2009","journal-title":"J. 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