Development and Validation of a Novel Calibration Methodology and Control Approach for Robot-Aided Transcranial Magnetic Stimulation (TMS).

OBJECTIVE: This article presents the development and validation of a new robotic system for Transcranial Magnetic Stimulation (TMS), characterized by a new control approach, and an ad-hoc calibration methodology, specifically devised for the TMS application. METHODS: The robotic TMS platform is composed of a 7 dof manipulator, controlled by an impedance control, and a camera-based neuronavigation system. The proposed calibration method was optimized on the workspace useful for the specific TMS application (spherical shell around the subject's head), and tested on three different hand-eye and robot-world calibration algorithms. The platform functionality was tested on six healthy subjects during a real TMS procedure, over the left primary motor cortex. RESULTS: employing our method significantly decreases ( ) the calibration error by 34% for the position and 19% for the orientation. The robotic TMS platform achieved greater orientation accuracy than the expert operators, significantly reducing orientation errors by 46% ( ). No significant differences were found in the position errors and in the amplitude of the motor evoked potentials (MEPs) between the robot-aided TMS and the expert operators. CONCLUSION: The proposed calibration represents a valid method to significantly reduce the calibration errors in robot-aided TMS applications. Results showed the efficacy of the proposed platform (including the control algorithm) in administering a real TMS procedure, achieving better coil positioning than expert operators, and similar results in terms of MEPs. SIGNIFICANCE: This article spotlights how to improve the performance of a robotic TMS platform, providing a reproducible and low-cost alternative to the few devices commercially available.

Different level of virtualization of sight and touch produces the uncanny valley of avatar's hand embodiment.

Humans increasingly often act through virtual and robotic avatars, which can feed back to their user only virtual sensory information. Since avatar is user's embodiment and body image is mostly based on senses, how virtualization of sensory inputs affects avatar self-attribution is a key question for understanding nowadays human behavior. By manipulating visual and tactile inputs in a series of experiments fashioned after the rubber hand illusion, we assessed the relative weight of the virtualization of sight (Real, Robotic, Virtual) and of touch (Real, Virtual) on artificial hand embodiment. Virtualization decreased embodiment, but unexpectedly lowest embodiment was found when only one sense was virtual. Discordant levels of virtualization of sight and touch elicited revulsion, extending the concept of the uncanny valley to avatar embodiment. Besides timing, spatial constraints and realism of feedback, a matched degree of virtualization of seen and felt stimuli is a further constraint in building the representation of the body.