title: Enhanced EMG signal processing for simultaneous and proportional myoelectric control.
abstract: A new signal processing scheme is presented for extracting neural control information from the multi-channel surface electromyographic signal (sEMG). The extracted information can be used to proportionally control a multi-degree of freedom (DOF) prosthesis. Four time-domain (TD) features were extracted from the multi-channel sEMG during a series of anisotonic, isometric wrist contractions, which involved simultaneous activations of the three DOF of the wrist. The forces produced at the three wrist DOFs during these contractions were also collected using a customized force sensor. The extracted features and the recorded force signals, as input/target pairs, were then used to train a multilayer perceptron (MLP) neural network. A five-fold cross-validation training/testing method was applied. The resulting performance is a significant improvement over a previously proposed sEMG processing method for the proportional, multi-DOF myoelectric control task.
authors: Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.
authors: Johnny L G
authors: JL
authors: Nielsen
authors: Steffen
authors: S
authors: Holmgaard
authors: Ning
authors: N
authors: Jiang
authors: Kevin
authors: K
authors: Englehart
authors: Dario
authors: D
authors: Farina
authors: Philip
authors: P
authors: Parker
mesh_terms: D000328:Adult; D000465:Algorithms; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D005260:Female; D006801:Humans; D007537:Isometric Contraction; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014953:Wrist / Q000503:physiopathology