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

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.

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Correspondence to Shalom Darmanjian or Jose Principe.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Darmanjian, S., Principe, J. Spatial-Temporal Clustering of Neural Data Using Linked-Mixtures of Hidden Markov Models. EURASIP J. Adv. Signal Process. 2009, 892461 (2010). https://doi.org/10.1155/2009/892461

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Keywords

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