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. 2020 Jul 28;10:12623. doi: 10.1038/s41598-020-69369-1

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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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Attention maps of CNNs shown with respect to the input sequences of HT and FT (top/bottom panels for HC/PD of two cases from the TS1 dataset, respectively). Top panel-HC case: (a) activation of CNN, (b) HT values, (cc) FT values; Bottom panel-PD case: (d) activation of CNN, (e) HT values, (f) FT values. Each sample represents a datum of the keystroke typing session, whereas the shadowed areas (blue and orange) denote the sample range that the neural-network model is using to make the prediction for the typing session.