  Non-invasive brain stimulation, such as transcranial alternating current
stimulation (tACS) provides a powerful tool to directly modulate brain
oscillations that mediate complex cognitive processes. While the body of
evidence about the effect of tACS on behavioral and cognitive performance is
constantly growing, those studies fail to address the importance of subject-
specific stimulation protocols. With this study here, we set the foundation to
combine tACS with a recently presented framework that utilizes real-time fRMI
and Bayesian optimization in order to identify the most optimal tACS protocol
for a given individual. While Bayesian optimization is particularly relevant to
such a scenario, its success depends on two fundamental choices: the choice of
covariance kernel for the Gaussian process prior as well as the choice of
acquisition function that guides the search. Using empirical (functional
neuroimaging) as well as simulation data, we identified the squared exponential
kernel and the upper confidence bound acquisition function to work best for our
problem. These results will be used to inform our upcoming real- time
experiments.
