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. 2023 Jan 13;13:744. doi: 10.1038/s41598-023-27978-6

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© The Author(s) 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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The figure shows a uniform manifold approximation and projection (UMAP) projection of a 30-min iEEG recording from a patient that was not included in the training, validation, or model testing process. The labels show gold-standard iEEG segments with 0-physiological iEEG, 1-interictal epileptiform discharges (IEDs). The separation between labels indicates that the autoencoder was able to generalize to novel data and cluster the data from two distinct categories to two minimally overlapping segments.