부산대학교 도서관

This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequentialparameter estimation (SPE) of the neural membrane time constant and input–output (IO) curve parameters in closed-loopelectromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performedby administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfiedor 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 optimizationand tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulsewidth, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomicalknowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The resultsconfirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By definingthe stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times forall 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 criticalpulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tunedoptimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loopEMG-guided cTMS.