Augmenting cognitive processes in robot-assisted motor rehabilitation

Cognitive processes, such as motor intention, attention, and higher level motivational states are important factors that govern motor performance and learning. Current robot-assisted rehabilitative programs focus only on the physical aspects of training. In this paper, we propose a framework for motor rehabilitation based on the augmentation of cognitive channels of patient-robot interactions and using it to deliver a more optimal therapy. By examining the cognitive processes involved in motor control and adaptation, it is argued that optimal therapy needs to be considered in the context of a complete motor scheme consisting not only of sensorimotor signals, but also their interactions with cognitive operations, such as motor planning, attention, and motivation, which mediate motor learning. We outline a few BCI-based modules for the detection and monitoring of relevant cognitive processes, which provide inputs for the robot to automatically modulate parameters of the rehabilitation protocol. Preliminary investigations on a BCI module for detection of motor intention, performed on a small group of stroke patients, show feasible accuracies.