Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation

S M Mahdi Alavi; Fidel Vila-Rodriguez; Adam Mahdi; Stefan M Goetz

doi:10.1007/s13534-022-00259-3

Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation

Biomed Eng Lett. 2022 Dec 30;13(2):119-127. doi: 10.1007/s13534-022-00259-3. eCollection 2023 May.

Authors

S M Mahdi Alavi¹, Fidel Vila-Rodriguez¹, Adam Mahdi^{2

3}, Stefan M Goetz^{4

5

6

7}

Affiliations

¹ The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada.
² Surrey Institute for People-Centred AI, University of Surrey, Surrey, UK.
³ Oxford Internet Institute, University of Oxford, Oxford, UK.
⁴ Department of Engineering, University of Cambridge, Cambridge, UK.
⁵ Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC USA.
⁶ Department of Electrical and Computer Engineering, Duke University, Durham, NC USA.
⁷ Department of Neurosurgery, Duke University, Durham, NC USA.

Abstract

This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.

Keywords: Brain stimulation; Closed-loop EMG-guided TMS; Controllable transcranial magnetic stimulation (cTMS); Input–output (IO) curve estimation; Variable stimulation pulse shape.

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