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

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.