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      Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning

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          Abstract

          Transcranial Magnetic Stimulation (TMS) can be used to map cortical motor topography by spatially sampling the sensorimotor cortex while recording Motor Evoked Potentials (MEP) with surface electromyography (EMG). Traditional sampling strategies are time-consuming and inefficient, as they ignore the fact that responsive sites are typically sparse and highly spatially correlated. An alternative approach, commonly employed when TMS mapping is used for presurgical planning, is to leverage the expertise of the coil operator to use MEPs elicited by previous stimuli as feedback to decide which loci to stimulate next. In this paper, we propose to automatically infer optimal future stimulus loci using active learning Gaussian Process-based sampling in place of user expertise. We first compare the user-guided (USRG) method to the traditional grid selection method and randomized sampling to verify that the USRG approach has superior performance. We then compare several novel active Gaussian Process (GP) strategies with the USRG approach. Experimental results using real data show that, as expected, the USRG method is superior to the grid and random approach in both time efficiency and MEP map accuracy. We also found that an active warped GP entropy and a GP random-based strategy performed equally as well as, or even better than, the USRG method. These methods were completely automatic, and succeeded in efficiently sampling the regions in which the MEP response variations are largely confined. This work provides the foundation for highly efficient, fully automatized TMS mapping, especially when considered in the context of advances in robotic coil operation.

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          Gaussian processes formachine learning

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            Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee.

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                Author and article information

                Contributors
                Role: Life Senior Member IEEE
                Role: Senior Member IEEE
                Journal
                101097023
                22433
                IEEE Trans Neural Syst Rehabil Eng
                IEEE Trans Neural Syst Rehabil Eng
                IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
                1534-4320
                1558-0210
                9 September 2021
                30 August 2021
                2021
                20 September 2021
                : 29
                : 1679-1689
                Affiliations
                Cognitive Systems Laboratory, Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA; Center for Signal Processing, Reasoning, and Learning (SPIRAL), Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA
                The Movement Neuroscience Laboratory, Department of Physical Therapy Rehabilitation and Movement Science, Northeastern University, Boston, MA 02115 USA.
                Cognitive Systems Laboratory, Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA; Center for Signal Processing, Reasoning, and Learning (SPIRAL), Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA
                Cognitive Systems Laboratory, Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA; Center for Signal Processing, Reasoning, and Learning (SPIRAL), Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA
                The Movement Neuroscience Laboratory, Department of Physical Therapy Rehabilitation and Movement Science, Northeastern University, Boston, MA 02115 USA.
                Cognitive Systems Laboratory, Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA; Center for Signal Processing, Reasoning, and Learning (SPIRAL), Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115 USA
                Author notes
                Corresponding author: Razieh Faghihpirayesh. raziehfaghih@ 123456ece.neu.edu
                Author information
                http://orcid.org/0000-0002-2680-5021
                http://orcid.org/0000-0002-2626-2039
                http://orcid.org/0000-0002-1114-3539
                Article
                NIHMS1736944
                10.1109/TNSRE.2021.3105644
                8452135
                34406942
                590547ee-bb76-4dd5-bdbe-f874dfc61c03

                This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

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                Categories
                Article

                active learning,entropy,gaussian process,machine learning,motor cortex,motor evoked potentials,transcranial magnetic stimulation

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