Coupling time decoding and trajectory decoding using a target-included model in the motor cortex

Authors:
Vernon Lawhern;Nicholas G. Hatsopoulos;Wei Wu
Affiliations:
Department of Statistics, Florida State University, Tallahassee, FL 32306-4330, USA;Department of Organismal Biology and Anatomy, Committees on Computational Neuroscience and Neurobiology, University of Chicago, Chicago, IL 60637, USA;Department of Statistics, Florida State University, Tallahassee, FL 32306-4330, USA
Venue:
Neurocomputing
Year:
2012

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Abstract

Significant progress has been made within the last decade in motor cortical decoding that predicts movement behaviors from population neuronal activity in the motor cortex. A majority of these decoding methods have focused on estimating a subject's hand trajectory in a continuous movement. We recently proposed a time identification decoding approach and showed that if a stereotyped movement is well represented by a sequence of targets (or landmarks), then the main structure of the movement can be reconstructed by detecting the reaching times at those targets. Both trajectory decoding and landmark-time decoding have their particular advantages, whereas a coupling of these two different strategies has not been examined. In this article we propose a synergy that comes from combining these two approaches for a stereotyped movement under a linear state-space framework. We develop a new decoding procedure based on a forward-backward propagation where the target is used in the initial stage in the backward step. Experimental results show that the new method significantly improves decoding accuracy over the non-target-included models. Furthermore, the coupling based on the new target-included method effectively combines the time decoding and trajectory decoding and further improves the decoding accuracy.