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Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces

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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).

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Correspondence to Jose C. Principe.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Darmanjian, S., Principe, J.C. Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces. EURASIP J. Adv. Signal Process. 2008, 216453 (2008) doi:10.1155/2008/216453

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Keywords

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