Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery

Brain-Computer Interfaces (BCIs) in combination with robot-assisted physical therapy may become a valuable tool for neurorehabilitation of patients with severe hemiparetic syndromes due to cerebrovascular brain damage (stroke) and other neurological conditions. A key aspect of this approach is reestablishing the disrupted sensorimotor feedback loop, i.e., determining the intended movement using a BCI and helping a human with impaired motor function to move the arm using a robot. It has not been studied yet, however, how artificially closing the sensorimotor feedback loop affects the BCI decoding performance. In this article, we investigate this issue in six healthy subjects, and present evidence that haptic feedback facilitates the decoding of arm movement intention. The results provide evidence of the feasibility of future rehabilitative efforts combining robot-assisted physical therapy with BCIs. Moreover, the results suggest that shared-control strategies in Brain-Machine Interfaces (BMIs) may benefit from haptic feedback.