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.

中文翻译：

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使用闭环贝叶斯优化有效地搜索大型 tACS 参数空间

背景 从众多可能性中选择最佳刺激参数是评估非侵入性脑刺激功效的主要障碍。目标我们证明贝叶斯优化可以快速搜索大参数空间并实时识别主题级刺激参数。方法 为了验证该方法，使用参与者对通过 tACS 引起的幻视强度的二元判断，采用贝叶斯优化。结果我们证明了贝叶斯优化在确定在短时间内（> 190 种可能性）最大化光幻视强度的参数方面的效率。我们的结果在三个蒙太奇中复制了频率依赖效应，并显示了光视感知的相位依赖效应。计算模型解释了这些相位效应是由到达视网膜的电流的建设性/破坏性干扰引起的。仿真分析证明了该方法对复杂响应函数的多功能性，即使考虑到噪声观测也是如此。结论 除了主观评级外，该方法还可用于优化基于行为和神经测量的 tACS 参数，并有可能用于为个人定制刺激方案。