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

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.