Papers Published

1. Kim, S-P; Sanchez, JC; Erdogmus, D; Rao, YN; Wessberg, J; Principe, JC; Nicolelis, M, Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models., Neural Networks : the Official Journal of the International Neural Network Society, vol. 16 no. 5-6 (2003), pp. 865-871 [S0893-6080(03)00108-4], [doi] .
   (last updated on 2023/06/01)

   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.

   Keywords:
   brain models;delays;divide and conquer methods;learning (artificial intelligence);least mean squares methods;man-machine systems;multilayer perceptrons;nonlinear network analysis;user interfaces;