title: Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.
abstract: This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.
authors.affiliation: Department of Electrical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Sung-Phil
authors.initials: SP
authors.lastname: Kim
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
authors.forename: Yadunandana N
authors.initials: YN
authors.lastname: Rao
authors.forename: Johan
authors.initials: J
authors.lastname: Wessberg
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
authors.forename: Miguel
authors.initials: M
authors.lastname: Nicolelis
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D017711:Nonlinear Dynamics