title: Extracting neural drives from surface EMG: a generative model and simulation studies.
abstract: A generative model for the surface EMG is presented. The model is built on the assumption that motor units in synergistic muscle share neural drives from spinal level, which correspond to the activation of different degrees of freedom (DOF) of natural movements, and are embedded within surface EMG. An artificial neural network (ANN) is developed to extract these drives simultaneously from the multi-channel surface EMG. A direct application of this technique would be providing control signals to prosthetic devices that are capable of simultaneous control of multiple DOF. It also has potential applications in the diagnosis and rehabilitation of spinal cord injuries and other neuromuscular disorders.
authors.affiliation: Institute of Biomedical Engineering, Department of Electrical and Computer Engineering, University of New Brunswick, Canada. ning.jiang@unb.ca
authors.forename: Ning
authors.initials: N
authors.lastname: Jiang
authors.forename: Philip A
authors.initials: PA
authors.lastname: Parker
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Englehart
mesh_terms: D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006801:Humans; D007596:Joints / Q000502:physiology; D008954:Models, Biological; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010988:Play and Playthings