Hierarchal decomposition of neural data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI

Shalom Darmanjian, Antonio Paiva, Jose Principe, Justin Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a simple algorithm that takes multidimensional neural input data and decomposes the joint likelihood into marginals using Boosted Mixtures of Hidden Markov Chains (BM-HMM). The algorithm applies techniques from boosting to create hierarchal dependencies between these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is weighted and incorporated into an ensemble decision. Our results show that this algorithm is very simple to train and computationally efficient, while also providing the ability to reduce the input dimensionality for Brain Machine Interfaces (BMIs).

Original languageEnglish (US)
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages3062-3067
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

Other2007 International Joint Conference on Neural Networks, IJCNN 2007
CountryUnited States
CityOrlando, FL
Period8/12/078/17/07

ASJC Scopus subject areas

  • Software

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    Darmanjian, S., Paiva, A., Principe, J., & Sanchez, J. (2007). Hierarchal decomposition of neural data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 3062-3067). [4371449] (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371449