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

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 5; pp. 1589 - 1600
• Main Authors: Noccaro, A., Mioli, A., D'Alonzo, M., Pinardi, M., Pino, G. Di, Formica, D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Calibration; calibration methodology; Cameras; Coils; Control algorithms; Control theory; Cortex (motor); End effectors; Evoked Potentials, Motor; hand-eye and robot-world calibration; Humans; impedance control; Magnetic fields; Motor evoked potentials; Neuronavigation; Operators; Orientation; Position errors; Robot arms; Robot control; Robot vision systems; Robotic TMS; Robotics; Robots; Scalp; Spherical shells; Transcranial Magnetic Stimulation
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3055434

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 (<inline-formula><tex-math notation="LaTeX">p< 0.001</tex-math></inline-formula>) 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% (<inline-formula><tex-math notation="LaTeX">p< 0.001</tex-math></inline-formula>). 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.