  Bayesian optimization has been proposed as a practical and efficient tool
through which to tune parameters in many difficult settings. Recently, such
techniques have been combined with real-time fMRI to propose a novel framework
which turns on its head the conventional functional neuroimaging approach. This
closed-loop method automatically designs the optimal experiment to evoke a
desired target brain pattern. One of the challenges associated with extending
such methods to real-time brain imaging is the need for adequate stopping
criteria, an aspect of Bayesian optimization which has received limited
attention. In light of high scanning costs and limited attentional capacities
of subjects an accurate and reliable stopping criteria is essential. In order
to address this issue we propose and empirically study the performance of two
stopping criteria.
