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Updated: Jan 22, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

Romy Lorenz1, Laura E Simmons2, Ricardo P Monti3

  • 1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK; Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany.

Brain Stimulation
|July 11, 2019
PubMed
Summary
This summary is machine-generated.

Bayesian optimization rapidly identifies optimal non-invasive brain stimulation parameters for individuals. This method efficiently searches vast parameter spaces, personalizing transcranial alternating current stimulation (tACS) protocols.

Keywords:
Bayesian optimizationExperimental designMachine-learningPhosphenesReal-timeTranscranial alternating current stimulation

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Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation tACS
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Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation tACS

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Selecting optimal stimulation parameters is a key challenge in non-invasive brain stimulation (NIBS) research.
  • Current methods for parameter selection are often time-consuming and inefficient.

Purpose of the Study:

  • To demonstrate the efficacy of Bayesian optimization for rapidly identifying subject-level stimulation parameters in real-time.
  • To validate Bayesian optimization using subjective phosphene intensity ratings in transcranial alternating current stimulation (tACS).

Main Methods:

  • Bayesian optimization was applied to search a large parameter space for tACS.
  • Participants provided binary judgments on elicited phosphene intensity to guide the optimization process.

Main Results:

  • Bayesian optimization efficiently identified parameters maximizing phosphene intensity within 5 minutes from over 190 possibilities.
  • Replicated known frequency-dependent effects and revealed novel phase-dependent effects, explained by current interference models.
  • Simulation analyses confirmed the method's robustness with complex response functions and noisy data.

Conclusions:

  • Bayesian optimization offers a rapid and efficient approach for optimizing tACS parameters.
  • This method can be extended to incorporate objective behavioral and neural measures for personalized stimulation protocols.