EURASIP Journal on Advances in Signal Processing
Volume 2008 (2008), Article ID 216453, 12 pages
doi:10.1155/2008/216453
Research Article

Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces

Shalom Darmanjian and Jose C. Principe

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA

Received 9 October 2007; Accepted 26 February 2008

Academic Editor: Aníbal Figueiras-Vidal

Copyright © 2008 Shalom Darmanjian and Jose C. Principe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose two algorithms that decompose the joint likelihood of observing multidimensional neural input data into marginal likelihoods. The first algorithm, boosted mixtures of hidden Markov chains (BMs-HMM), applies techniques from boosting to create implicit hierarchic dependencies between these marginal subspaces. The second algorithm, linked mixtures of hidden Markov chains (LMs-HMM), uses a graphical modeling framework to explicitly create the hierarchic dependencies between these marginal subspaces. Our results show that these algorithms are very simple to train and computationally efficient, while also reducing the input dimensionality for brain-machine interfaces (BMIs).