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Syst."],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p> Cross-user variability is a well-known challenge that leads to severe performance degradation and impacts the robustness of practical myoelectric control systems. To address this issue, a novel method for myoelectric recognition of finger movement patterns is proposed by incorporating a neural decoding approach with unsupervised domain adaption (UDA) learning. In our method, the neural decoding approach is implemented by extracting microscopic features characterizing individual motor unit (MU) activities obtained from a two-stage online surface electromyogram (SEMG) decomposition. A specific deep learning model is designed and initially trained using labeled data from a set of existing users. The model can update adaptively when recognizing the movement patterns of a new user. The final movement pattern was determined by a fuzzy weighted decision strategy. SEMG signals were collected from the finger extensor muscles of 15 subjects to detect seven dexterous finger-movement patterns. The proposed method achieved a movement pattern recognition accuracy of ([Formula: see text])% over seven movements under cross-user testing scenarios, much higher than that of the conventional methods using global SEMG features. Our study presents a novel robust myoelectric pattern recognition approach at a fine-grained MU level, with wide applications in neural interface and prosthesis control. <\/jats:p>","DOI":"10.1142\/s0129065725500145","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T07:04:08Z","timestamp":1735283048000},"source":"Crossref","is-referenced-by-count":3,"title":["Online and Cross-User Finger Movement Pattern Recognition by Decoding Neural Drive Information from Surface Electromyogram"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7712-6417","authenticated-orcid":false,"given":"Haowen","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230002, P. R. 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