Transcranial magnetic stimulation (TMS) is a noninvasive medical procedure that can modulate brain activity, and it is widely used in neuroscience and neurology research. Compared to manual operators, robots may improve the outcome of TMS due to their superior accuracy and repeatability. However, there has not been a widely accepted standard protocol for performing robotic TMS using fine-segmented brain images, resulting in arbitrary planned angles with respect to the true boundaries of the modulated cortex. Given that the recent study in TMS simulation suggests a noticeable difference in outcomes when using different anatomical details, cortical shape should play a more significant role in deciding the optimal TMS coil pose. In this work, we introduce an image-guided robotic system for TMS that focuses on (1) establishing standardized planning methods and heuristics to define a reference (true zero) for the coil poses and (2) solving the issue that the manual coil placement requires expert hand-eye coordination which often leading to low repeatability of the experiments. To validate the design of our robotic system, a phantom study and a preliminary human subject study were performed. Our results show that the robotic method can half the positional error and improve the rotational accuracy by up to two orders of magnitude. The accuracy is proven to be repeatable because the standard deviation of multiple trials is lowered by an order of magnitude. The improved actuation accuracy successfully translates to the TMS application, with a higher and more stable induced voltage in magnetic field sensors.

Objective: This study shows the force/torque control strategy for the robotized TMS system whose TMS coil's floor is incurved. The strategy considered the adhesion and friction between the coil and the subject's head. Methods: Hybrid position/force control and proportional torque were used for the strategy. The force magnitude applied for the force control was scheduled by the error between the coil's current position and the target point. Results: The larger desired force for the force controller makes the error quickly. By scheduling the force magnitude applied for the force control, the low error between the coil's current and target positions is maintained with the relatively small force after the larger force is applied for around 10 seconds. The proportional torque made the adhesion better by locating the contact area between the coil and the head close to the coil. I was shown by checking the ${\tau}_c/F_c$ value from the experimental results. While the head slowly moved away from the coil during the TMS treatment, the coil still interacted with the head. Using that characteristic, the coil could locate the new target point using the force/torque strategy without any trajectory planning. Conclusion: The proposed force/torque controller enhanced the adhesion between the incurved TMS coil and the subject's head. It also reduced the error quickly by scheduling the magnitude of the force applied. Significance: This study proposes the robotized TMS system's force/torque control strategy considering the physical characteristics from the contact between the incurved TMS coil case and the subject's head.