Papers Published

1. Sung-Phil Kim and Sanchez, J.C. and Erdogmus, D. and Rao, Y.N. and Wessberg, J. and Principe, J.C. and Nicolelis, M., Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models, Neural Netw. (UK), vol. 16 no. 5-6 (2003), pp. 865 - 71 [S0893-6080(03)00108-4] .
   (last updated on 2007/04/15)

   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;