Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

ABSTRACT:

Background

Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation.

Objective

We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time.

Methods

To validate the method, Bayesian optimization was employed using participants' binary judgements about the intensity of phosphenes elicited through tACS.

Results

We demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects

SUBMITTER: Lorenz R 

PROVIDER: S-EPMC6879005 | biostudies-literature | 2019 Nov - Dec

REPOSITORIES: biostudies-literature