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

Alavi, Seyed Mohammad Mahdi; Vila‐Rodriguez, Fidel; Mahdi, Adam; Goetz, Stefan M.

doi:10.1007/s13534-022-00259-3

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…The stopping rule is arbitrarily defined to satisfy the convergence criterion (6) with ϵ = 0.001 for T = 5 successive times. As discussed in [31], [34]- [36], there is a trade-off between the estimation accuracy (i.e., ϵ and T values) and the number of samples in a successful termination. Reducing the convergence tolerance and increasing the successive times parameter would improve the estimation, however, more SD data are needed to meet the stopping rule.…”

Section: Resultsmentioning

confidence: 99%

“…In [31], [34]- [36], the FIM optimization was developed for closed-loop estimation of the neural input-output curve and activation dynamics including the membrane time constant and coupling gain. No paper or study has been published on the FIM-based closed-loop SD curve estimation, and comparison with the uniform and random methods.…”

Section: A the Contributions Of This Papermentioning

confidence: 99%

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Closed-Loop Estimation of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization and Comparison With Uniform and Random Methods

Alavi

2023

Preprint

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Background: Strength-duration (SD) curve, rheobase and chronaxie parameters provide insights about the interdependence between stimulus strength and stimulus duration (or pulse width), and the neural activation dynamics such as the membrane time constant, which are useful for diagnostics and therapeutic applications. The existing SD curve estimation methods are based on open-loop uniform and/or random selection of the pulse widths. Objective: To develop a method for closed-loop estimation of the SD curve. Method: In the proposed method, after the selection of each pulse width through Fisher information matrix (FIM) optimization, the corresponding motor threshold (MT) is computed, the SD curve estimation is updated, and the process continues until satisfaction of a stopping rule based on the successive convergence of the SD curve parameters. The results are compared with various uniform methods where pulse widths are chosen in ascending, descending and random orders, and with methods with two and all non-uniform random pulse widths. Results: 160 simulation cases were run. The FIM method satisfied the stopping rule in 144 runs, and estimated the rheobase (chronaxie in parenthesis) with an average absolute relative error (ARE) of 1.73% (2.46%), with an average of 82 samples. At this point, methods with two and all random pulse widths, and uniform methods with descending, ascending and random orders led to 5.66% (20.27%), 2.15% (4.51%), 8.57% (54.96%), 3.52% (5.45%), and 2.19% (4.40%) AREs, which are greater than that achieved through the FIM method. In all 160 runs, The FIM method has chosen the minimum and maximum pulse widths as the optimal pulse widths. Conclusions: The SD curve is identifiable by acquiring the SD data from the minimum and maximum pulse widths achieved through the FIM optimization. The SD data at random or uniform pulse widths from only the vertical area or lower plateau of the curve might not result in satisfactory estimation. Significance: This paper provides insights about pulse widths selection in closed-loop and open-loop SD curve estimation methods.

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Section: Resultsmentioning

confidence: 99%

Section: A the Contributions Of This Papermentioning

confidence: 99%

Closed-Loop Estimation of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization and Comparison With Uniform and Random Methods

Alavi

2023

Preprint

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Towards real-time EEG–TMS modulation of brain state in a closed-loop approach

Humaidan,

Xu,

Kirchhoff

et al. 2024

Clinical Neurophysiology

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Identifiability Analysis and Noninvasive Online Estimation of the First-Order Neural Activation Dynamics in the Brain With Closed-Loop Transcranial Magnetic Stimulation

Alavi

Mahdi

Vila‐Rodriguez

et al. 2023

IEEE Trans. Biomed. Eng.

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Background: Neurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. Objective: To develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. Method: First, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. Results: The results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves' lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. Conclusions: SPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. Significance: The proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

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

Cited by 3 publications

References 62 publications

Closed-Loop Estimation of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization and Comparison With Uniform and Random Methods

Closed-Loop Estimation of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization and Comparison With Uniform and Random Methods

Towards real-time EEG–TMS modulation of brain state in a closed-loop approach

Identifiability Analysis and Noninvasive Online Estimation of the First-Order Neural Activation Dynamics in the Brain With Closed-Loop Transcranial Magnetic Stimulation

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