Spatial-Temporal Clustering of Neural Data Using Linked-Mixtures of Hidden Markov Models. (12th January 2010)

• Record Type: Journal Article
• Authors: Darmanjian, Shalom; Principe, Jose
• Other Names: Johnson Don Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2009(2009)
• Publication Date: 2010-01-12
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2009/892461
• Languages: English
• ISSNs: 1687-6172

Abstract

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.