# Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization

arXiv: https://arxiv.org/abs/1511.07827 · HTML: https://mlsanity.com/paper/1511.07827/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2015.

## Built on by (most cited first, 9 of 9)

- [Automatic Termination for Hyperparameter Optimization](https://mlsanity.com/paper/2104.08166/) (2021)
- [Overtuning in Hyperparameter Optimization](https://mlsanity.com/paper/2506.19540/) (2025)
- [Cost-aware Stopping for Bayesian Optimization](https://mlsanity.com/paper/2507.12453/) (2025)
- [Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression](https://mlsanity.com/paper/2204.04831/) (2022)
- [Designing MacPherson Suspension Architectures using Bayesian Optimization](https://mlsanity.com/paper/2206.09022/) (2022)
- [Pareto Optimization of a Laser Wakefield Accelerator](https://mlsanity.com/paper/2303.15825/) (2023)
- [A Probabilistic Framework for Modular Continual Learning](https://mlsanity.com/paper/2306.06545/) (2023)
- [Regret-Based (ε,δ)-optimal Stopping Criteria for Bayesian Optimization](https://mlsanity.com/paper/2605.22561/) (2026)
- [Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation](https://mlsanity.com/paper/2609.13396/) (2026)

## Abstract (arXiv)

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.