1. Closed-Loop phase selection in EEG-TMS using Bayesian Optimization
- Author
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Kirchhoff, Miriam, Humaidan, Dania, and Ziemann, Ulf
- Subjects
Quantitative Biology - Neurons and Cognition ,Electrical Engineering and Systems Science - Signal Processing ,I.2.1 ,J.3 - Abstract
Research on transcranial magnetic stimulation (TMS) combined with encephalography feedback (EEG-TMS) has shown that the phase of the sensorimotor mu rhythm is predictive of corticospinal excitability. Thus, if the subject-specific optimal phase is known, stimulation can be timed to be more efficient. In this paper, we present a closed-loop algorithm to determine the optimal phase linked to the highest excitability with few trials. We used Bayesian optimization as an automated, online search tool in an EEG-TMS simulation experiment. From a sample of 38 participants, we selected all participants with a significant single-subject phase effect (N = 5) for simulation. We then simulated 1000 experimental sessions per participant where we used Bayesian optimization to find the optimal phase. We tested two objective functions: Fitting a sinusoid in Bayesian linear regression or Gaussian Process (GP) regression. We additionally tested adaptive sampling using a knowledge gradient as the acquisition function compared with random sampling. We evaluated the algorithm's performance in a fast optimization (100 trials) and a long-term optimization (1000 trials). For fast optimization, the Bayesian linear regression in combination with adaptive sampling gives the best results with a mean phase location accuracy of 79 % after 100 trials. With either sampling approach, Bayesian linear regression performs better than GP regression in the fast optimization. In the long-term optimization, Bayesian regression with random sampling shows the best trajectory, with a rather steep improvement and good final performance of 87 % mean phase location accuracy. We show the suitability of closed-loop Bayesian optimization for phase selection. We could increase the speed and accuracy by using prior knowledge about the expected function shape compared with traditional Bayesian optimization with GP regression., Comment: 6 pages, 4 figures. This work was accepted by the International Conference on Systems, Man, and Cybernetics, Kuching, Malaysia, 2024. \copyright 2024 IEEE
- Published
- 2024