Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces. (6th May 2008)

• Record Type: Journal Article
• Authors: Darmanjian Darmanjian, Shalom Shalom; Principe Principe, Jose C. Jose C.
• Other Names: Figueiras-Vidal Figueiras-Vidal Aníbal Aníbal Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2008-05-06
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/216453
• Languages: English
• ISSNs: 1687-6172

Abstract

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).