Application of system identification methods for decoding imagined single-joint movements in an individual with high tetraplegia

This study investigated the decoding of imagined arm movements from M1 in an individual with high level tetraplegia. The participant was instructed to imagine herself performing a series of single-joint arm movements, aided by the visual cue of an animate character performing these movements. System identification was used offline to predict the trajectories of the imagined movements and compare these predictions to the trajectories of the actual movements. We report rates of 25 - 50% for predicting completely imagined arm movements in the absence of a priori movements to aid in decoder building.