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Mapping Intrinsic and Extrinsic Muscle Myoelectric Activity During Natural Dynamic Movements Into Finger and Wrist Kinematics Using Deep Learning Prediction Models., , , and . IEEE Trans. Hum. Mach. Syst., 53 (5): 924-934 (October 2023)Multi-Attention Feature Fusion Network for Accurate Estimation of Finger Kinematics From Surface Electromyographic Signals., , , , , , and . IEEE Trans. Hum. Mach. Syst., 53 (3): 512-519 (2023)A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings., , , , , and . Int. J. Neural Syst., 27 (6): 1750025:1-1750025:18 (2017)Theoretical Model and Experimental Validation of the estimated proportions of common and independent input to motor neurons., , and . EMBC, page 254-257. IEEE, (2015)Linear predictive coding of myoelectric signals., , , and . ICASSP (5), page 629-632. IEEE, (2005)Extending mode switching to multiple degrees of freedom in hand prosthesis control is not efficient., , , and . EMBC, page 658-661. IEEE, (2014)Power spectrum of the rectified EMG: Influence of motor unit action potential shapes., , and . EMBC, page 2193-2196. IEEE, (2014)Simulations of high-density surface electromyograms in dynamic muscle contractions., , and . EMBC, page 3453-3456. IEEE, (2017)Biomedical Signal Compression With Optimized Wavelets., , , , and . ICASSP (2), page 1044-1047. IEEE, (2006)Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis., , , , and . Biomed. Signal Process. Control., 3 (4): 300-304 (2008)