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Motor impairments come in different forms. One class of motor impairments, relates to accuracy of tracking a moving object, as, for instance, when chasing in an attempt to catch it. Here we look at neural signals associated with errors in tracking, and the implications for brain-computer-interfaces that target impairment-tailored rehabilitation. As a starting point, we characterized EEG signals evoked by tracking errors during continuous natural motion, in healthy participants. Participants played a virtual 3D, ecologically valid haptic tennis game, and had to track a moving tennis ball in order to hit and send the ball towards the opponent's court. Sudden changes in the motion of the tennis ball elicited error related potentials. These were characterized by a negative peak at 135 msec and two positive peaks at 211 and 336 msec. The negative peak had a parietal scalp distribution, and the positive had a centro-frontal distribution. sLORETA source estimation for the peaks suggested brain activity in the somatosensory, motor, visual and anterior cingulate cortex. Implications are double: changes in the error potential characteristics provide an assessment strategy for rehabilitation; and the identified error potential can be used in the Brain computer interface feedback loop for tailored rehabilitation. Taken together, these results provide a methodology of rehabilitation systems specifically tailored to the unique impairment.

Abstract. We investigated brain potentials recorded by electroencephalography (EEG) signals in response to unpredictable haptic/kinesthetic disturbances to a continuously moving object in a hapto-visual 3D virtual world that highly resembles reality. Participants moved a virtual object from an initial position to a target position in the virtual environment. A large cylinder obscured part of the motion between the origin and the target. The position of the emerging object under the cylinder is disturbed, and hence unexpected, for part of the scenarios. This disturbance is perceived as an error. We examined the EEG signals locked to the error. Our results show a consistent disturbance-locked potential with an early negative peak followed by a positive peak. Peak-to-peak amplitude increased with the disturbance magnitude. Source estimation at the time of the negative and positive peaks revealed a strong activity in the vicinity of Brodmann area (BA) 7, known to be involved in hapto-visual integration and in the neural computation of dynamic motor errors. These results demonstrate the presence of haptic-disturbance-related brain activity under conditions of continuous motion. Results further suggest a feedback signal for error detection and correction in EEG-based Brain-Computer Interfaces (BCI) in applications such as telesurgery, manipulation of remote objects and rehabilitation.