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Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

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Bibliographic Details
Title: Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.
Authors: Postepski F; Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Akademicka 9, 20-031, Lublin, Poland. filip.postepski@mail.umcs.pl., Wojcik GM; Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Akademicka 9, 20-031, Lublin, Poland., Wrobel K; Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Akademicka 9, 20-031, Lublin, Poland., Kawiak A; Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, Akademicka 9, 20-031, Lublin, Poland., Zemla K; Institute of Psychology, SWPS University, Chodakowska 19/31, Warsaw, 03-815, Poland., Sedek G; Institute of Psychology, SWPS University, Chodakowska 19/31, Warsaw, 03-815, Poland.
Source: Scientific reports [Sci Rep] 2025 Mar 27; Vol. 15 (1), pp. 10521. Date of Electronic Publication: 2025 Mar 27.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-025-92378-x