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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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Neural network encoder-decoder architecture. The figure shows a block diagram of neural network encoder-decoder architecture utilizing an attention mechanism for unsupervised iEEG feature extraction. Input consists of iEEG data spectrograms propagated through gated recurrent units (GRU) encoder providing low-dimensional embedding space. Subsequently, the GRU decoder reconstructs the input spectrograms while minimizing the unsupervised loss function (mean square error).