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Abstract

A neurorehabilitation approach that combines robot-assisted active physical therapy and Brain-Computer Interfaces (BCIs) may provide an additional mileage with respect to traditional rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage (e.g., stroke) and other neurological conditions. In this paper, we describe the design and modes of operation of a robot-based rehabilitation framework that enables artificial support of the sensorimotor feedback loop. The aim is to increase cortical plasticity by means of Hebbian-type learning rules. A BCI-based shared-control strategy is used to drive a Barret WAM 7-degree-of-freedom arm that guides a subject's arm. Experimental validation of our setup is carried out both with healthy subjects and stroke patients. We review the empirical results which we have obtained to date, and argue that they support the feasibility of future rehabilitative treatments employing this novel approach.