The effect of feedback presentation on motor imagery performance during BCI-teleoperation of a humanlike robot

Users of a brain-computer interface (BCI) learn to co-adapt with the system through the feedback they receive. Particularly in case of motor imagery BCIs, feedback design can play an important role in the course of motor imagery training. In this paper we investigated the effect of biased visual feedback on performance and motor imagery skills of users during BCI control of a pair of humanlike robotic hands. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects' online performance, evaluation of brain activity patterns revealed that subjects' self-regulation of motor imagery features improved due to a positive bias of feedback. We discuss how this effect could be possibly due to the humanlike design of feedback and occurrence of body ownership illusion. Our findings suggest that in general training protocols for BCIs, realistic feedback design and subject's self-evaluation of performance can play an important role in the optimization of motor imagery skills.