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 of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations.

Conclusion: Alongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals.

Keywords: Bayesian optimization; Experimental design; Machine-learning; Phosphenes; Real-time; Transcranial alternating current stimulation.