Target-included model and hybrid decoding of stereotyped hand movement in the motor cortex

A number of decoding methods, varying from common linear Gaussian models to more complicated point process frameworks, have been developed to infer hand movement from neuronal firing activity in the motor cortex. Most of these methods focus on estimating subjectpsilas hand trajectory in a continuous movement. We recently proposed a template-based 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 will be better addressed by detecting the reaching times at those targets. Both trajectory decoding and landmark-time decoding have advantages respectively, whereas a coupling of these two different strategies has not been examined. Here we propose a synergy that comes from combining these two approaches for a stereotyped movement under a state-space framework, where the recordings were made in the arm area of primary motor cortex in an awake behaving monkey using a chronically implanted multi-electrode array. We at first identify the target times using the template-based method. Then we include the detected targets as a linear control input in the kinematic model of the state-space formulation. Such an inclusion is justified by the empirical linear relationship between the kinematics and target positions. Experimental results show that the hybrid model includes the benefits from both approaches and significantly improves the decoding accuracy.