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This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants\u2014personalized Bayesian optimization (pBO)\u2014that searches available parameter combinations to optimize an intervention as a function of an individual\u2019s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject\u2019s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008886","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T14:18:55Z","timestamp":1631197135000},"page":"e1008886","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":41,"title":["Personalized brain stimulation for effective neurointervention across participants"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0198-7252","authenticated-orcid":true,"given":"Nienke E. 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