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It is necessary to study how they can be exploited effectively for a more accurate extraction. In response to the problem, an experiment is designed that establishes the relationship between sEMG signals and forearm muscles based on signal-to-noise ratio (SNR). The SNR of sEMG signals in different sampling channels can be calculated and compared, and then the potential contribution of each channel during different hand motions will be evaluated comprehensively. The active muscle regions can be obtained from the established relationship that is a useful reference for feature extraction. Finally, the relations between the computational cost, channel numbers and electrode layouts are explored. The findings of this paper support the idea that the accuracy of pattern recognition will not be affected when reducing the redundant electrodes. <\/jats:p>","DOI":"10.1142\/s0219843619410068","type":"journal-article","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T04:16:19Z","timestamp":1564373779000},"page":"1941006","source":"Crossref","is-referenced-by-count":8,"title":["An Optimized Selection Method of Channel Numbers and Electrode Layouts for Hand Motion Recognition"],"prefix":"10.1142","volume":"16","author":[{"given":"Jiang","family":"Hua","sequence":"first","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Research Center of Biologic Manipulator and Intelligent Measurement and Control, Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, P. R. 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