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  • Research Article
  • Open Access

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

EURASIP Journal on Advances in Signal Processing20102009:892461

https://doi.org/10.1155/2009/892461

  • Received: 1 February 2009
  • Accepted: 19 November 2009
  • Published:

Abstract

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.

Keywords

  • Markov Model
  • Synthetic Data
  • Neural Model
  • Publisher Note
  • Disable Patient

Publisher note

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Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA

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