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. 2020 Mar 2;10:3817. doi: 10.1038/s41598-020-60661-8

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Topomaps displaying the correlations between resting-state EEG power and Python learning outcomes across electrode locations and scatterplots showing data concatenated across right fronto-temporal networks (F8, FC6, T8). (A) Correlations between mean beta power (13–29.5 Hz) and Python learning rate across channels, with the relation between mean fronto-temporal beta power and learning rate depicted in scatter plot. (B) Correlations between mean low-gamma power (30–40 Hz) and post-test declarative knowledge across channels, with the relation between mean fronto-temporal low-gamma power and declarative knowledge depicted in scatter plot.