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. 2022 Sep 26;12(10):1294. doi: 10.3390/brainsci12101294

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© 2022 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Human-in-the-loop optimization using a covariance matrix adaptation evolution strategy (CMA--ES): (A) tOCS parameters: DC intensity in mA (blue), AC amplitude in mA (red), and AC frequency in Hz (black). (B) tACS parameters: DC intensity = 0 mA (blue), AC amplitude in mA (red), and AC frequency in Hz (black). (C) Best cost (i.e., negative steady-state gain of HbT in M) for tOCS over 22 iterations of CMA--ES. (D) Best cost (HbT in M) for tACS over 22 iterations of CMA--ES.

Human-in-the-loop optimization using a covariance matrix adaptation evolution strategy (CMA--ES): (A) tOCS parameters: DC intensity in mA (blue), AC amplitude in mA (red), and AC frequency in Hz (black). (B) tACS parameters: DC intensity = 0 mA (blue), AC amplitude in mA (red), and AC frequency in Hz (black). (C) Best cost (i.e., negative steady-state gain of HbT in M) for tOCS over 22 iterations of CMA--ES. (D) Best cost (HbT in M) for tACS over 22 iterations of CMA--ES.