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. 2022 Feb 16;12:2650. doi: 10.1038/s41598-022-05810-x

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© The Author(s) 2022

Open AccessThis 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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Flowchart of the proposed neural encdoer-decoder modeling framework for PERCLOS. This framework has two main steps: encoder model, and decoder model. In the encoder model, state transition process of PERCLOS and the relationship between every EEG feature and PERCLOS are defined. Biomarkers are obtained using t-test and they are used in the decoder step to predict the PERCLOS in real-time using Bayes filtering.