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Hierarchical clustering of neural data using Linked-Mixtures of Hidden Markov Models for Brain Machine Interfaces

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2 Author(s)
Darmanjian, S. ; Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL ; Principe, J.

In this paper, we build upon previous brain machine interface (BMI) signal processing models that require a-priori 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. Given that BMIs must work with disabled patients who lack arm kinematic information, the clustering work describe within this paper is very relevant for future BMIs.

Published in:

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Date of Conference:

19-24 April 2009