Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation

TL;DR

This paper introduces a method to improve brain activity reconstruction by using sequential kriging optimization with preemptive termination to efficiently identify optimal solver parameters, reducing unnecessary computations.

Contribution

It proposes a novel preemptive termination strategy within sequential kriging optimization for brain activity simulation, enhancing efficiency in parameter selection.

Findings

Preemptive termination reduces computation time.

Method improves efficiency without sacrificing solution quality.

Numerical experiments confirm benefits of the approach.

Abstract

Reconstructing brain activity through electroencephalography requires a boundary value problem (BVP) solver to take a proposed distribution of current dipoles within the brain and compute the resulting electrostatic potential on the scalp. This article proposes the use of sequential kriging optimization to identify different optimal BVP solver parameters for dipoles located in isolated sections of the brain by considering the cumulative impact of randomly oriented dipoles within a chosen isolated section. We attempt preemptive termination of parametrizations suggested during the sequential kriging optimization which, given the results to that point, seem unlikely to produce high quality solutions. Numerical experiments on a simplification of the full geometry for which an approximate solution is available show a benefit from this preemptive termination.