{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:15:39Z","timestamp":1771002939957,"version":"3.50.1"},"reference-count":26,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51675123"],"award-info":[{"award-number":["51675123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603112"],"award-info":[{"award-number":["61603112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51521003"],"award-info":[{"award-number":["51521003"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Human. Robot."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> Deep learning (DL) has made tremendous contributions to image processing. Recently, the DL has also attracted attention in the specialized field of neural decoding from raw myoelectric signals (electromyograms, EMGs). However, to our knowledge, most existing methods require some measure of preprocessing of the raw EMGs. Moreover, research to date has not accounted for the variability in the signal during time sequences. In this paper, we propose a new convolutional neural network (CNN) structure that can directly process raw EMG signals for hand gesture classification. More specifically, we assess the effects of various window sizes and of two different EMG representations (time sequence and frequency spectra) on open EMG datasets. We found that the frequency spectra derived from raw EMGs is more suitable as the model input in the task of gesture classification. Meanwhile, the combination use of long window could improve the classification accuracy (CA) and the window of 1024 ms achieved the best results on two open datasets ([Formula: see text]% and [Formula: see text]%). Further, our model requires no feature extraction procedures and is comparable with the optimal combination of features and classifier used by the traditional methods in the performance of specific tasks. <\/jats:p>","DOI":"10.1142\/s0219843619500130","type":"journal-article","created":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T04:20:05Z","timestamp":1561609205000},"page":"1950013","source":"Crossref","is-referenced-by-count":20,"title":["EMG Pattern Recognition Using Convolutional Neural Network with Different Scale Signal\/Spectra Input"],"prefix":"10.1142","volume":"16","author":[{"given":"Wei","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Technology Innovation Building, No. 2 Yikuang Street, Harbin 150080, P. R. 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