A State-Space Framework for Movement Control to Dynamic Goals Through Brain-Driven Interfaces

Abstract

State-space estimation is a convenient framework for the design of brain-driven interfaces, where neural activity is used to control assistive devices for individuals with severe motor deficits. Recently, state-space approaches were developed to combine goal planning and trajectory-guiding neural activity in the control of reaching movements of an assistive device to static goals. In this paper, we extend these algorithms to allow for goals that may change over the course of the reach. Performance between static and dynamic goal state equations and a standard free movement state equation is compared in simulation. Simulated trials are also used to explore the possibility of incorporating activity from parietal areas that have previously been associated with dynamic goal position. Performance is quantified using mean-square error (MSE) of trajectory estimates. We also demonstrate the use of goal estimate MSE in evaluating algorithms for the control of goal-directed movements. Finally, we propose a framework to combine sensor data and control algorithms along with neural activity and state equations, to coordinate goal-directed movements through brain-driven interfaces

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