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

doi:10.1109/IJCNN.2007.4371449

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

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages	3062-3067
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4371449
State	Published - Dec 1 2007
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: Aug 12 2007 → Aug 17 2007

Publication series

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

Other

Other	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country	United States
City	Orlando, FL
Period	8/12/07 → 8/17/07

ASJC Scopus subject areas

Software

Access to Document

10.1109/IJCNN.2007.4371449

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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

Hierarchal decomposition of neural data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI. / Darmanjian, Shalom; Paiva, Antonio; Principe, Jose; Sanchez, Justin.

The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 3062-3067 4371449 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 4371449, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 3062-3067, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 8/12/07. https://doi.org/10.1109/IJCNN.2007.4371449

Darmanjian S, Paiva A, Principe J, Sanchez J. 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. 2007. p. 3062-3067. 4371449. (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371449

Darmanjian, Shalom ; Paiva, Antonio ; Principe, Jose ; Sanchez, Justin. / Hierarchal decomposition of neural data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI. The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. pp. 3062-3067 (IEEE International Conference on Neural Networks - Conference Proceedings).

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