Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.
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.
Kim
Sung-Phil
SP
Department of Electrical Engineering, University of Florida, Gainesville, FL 32611, USA.
Sanchez
Justin C
JC
Erdogmus
Deniz
D
Rao
Yadunandana N
YN
Wessberg
Johan
J
Principe
Jose C
JC
Nicolelis
Miguel
M
D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D017711:Nonlinear Dynamics