title: Predicting EMG with generalized Volterra kernel model.
abstract: Generalized Volterra kernel model (GVM) is developed in spirits of the generalized linear model (GLM) and used to predict EMG signals based on M1 cortical spike trains during a prehension task. The GVM for EMG consists of a cascade of a multiple-input-single-output Volterra kernel model (VM) and an exponential activation function. Without loss of generality, the exponential activation function constrains the unbounded VM output within the positive range, which fully covers the dynamic range of the rectified EMG signals. Results show that GVMs are more accurate than the VMs due to this asymptotic property.
authors.affiliation: Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089, USA. dsong@usc.edu
authors.forename: Dong
authors.initials: D
authors.lastname: Song
authors.forename: Phillip
authors.initials: P
authors.lastname: Hendrickson
authors.forename: Vasilis Z
authors.initials: VZ
authors.lastname: Marmarelis
authors.forename: Jose
authors.initials: J
authors.lastname: Aguayo
authors.forename: Jiping
authors.initials: J
authors.lastname: He
authors.forename: Gerald E
authors.initials: GE
authors.lastname: Loeb
authors.forename: Theodore W
authors.initials: TW
authors.lastname: Berger
mesh_terms: D000465:Algorithms; D000818:Animals; D001132:Arm / Q000502:physiology; D001185:Artificial Intelligence; D004576:Electromyography / Q000379:methods*; D008253:Macaca mulatta; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*