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  • Research Article
  • Open Access

Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces

EURASIP Journal on Advances in Signal Processing20082008:216453

  • Received: 9 October 2007
  • Accepted: 26 February 2008
  • Published:


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).


  • Information Technology
  • Input Data
  • Markov Chain
  • Quantum Information
  • Graphical Modeling

Publisher note

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Authors’ Affiliations

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


© S. Darmanjian and J. C. Principe. 2008

This article is published under license to BioMed Central Ltd. 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.