A brain-machine interface architecture to control an upper limb rehabilitation exoskeleton

Abstract

The combination of Brain-Machine Interfaces (BMIs) with assistive technologies has seen a rapid development during the last few years. Regarding post-stroke rehabilitation, the BMI could be combined with other sensors to assist movements performed by the patient by attaching an exoskeleton to the affected arm. To that end, the patient’s arm movement intentions can be obtained by processing the brain information and generating suitable output commands to control the exoskeleton kinematics. In this paper, we propose an architecture that combines a Brain-Machine Interface with an upper limb exoskeleton. Two different experimental setups based on flexion/extension movements of the arm are proposed.