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Spatial-Temporal Clustering of Neural Data Using Linked-Mixtures of Hidden Markov Models

EURASIP Journal on Advances in Signal Processing20102009:892461

Received: 1 February 2009

Accepted: 19 November 2009

Published: 12 January 2010


This paper builds upon the previous Brain Machine Interface (BMI) signal processing models that require apriori knowledge about the patient's arm kinematics. Specifically, we propose an unsupervised hierarchical clustering model that attempts to discover both the interdependencies between neural channels and the self-organized clusters represented in the spatial-temporal neural data. Results from both synthetic data generated with a realistic neural model and real BMI data are used to quantify the performance of the proposed methodology. Since BMIs must work with disabled patients who lack arm kinematic information, the clustering work described within this paper is relevant for future BMIs.


Markov ModelSynthetic DataNeural ModelPublisher NoteDisable Patient

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Authors’ Affiliations

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


© S. Darmanjian and J. Principe. 2009

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.