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Intelligent systems. Theory and applications, 2021, Volume 25, Issue 4, Pages 318–321 (Mi ista472)  
This article is cited in 2 scientific papers (total in 2 papers)

Part 8. Human-oriented artificial intelligence and neural interface technologies

Neural network classifier for EEG data from people who have undergone COVID-19 and have not

A. Zubovab, M. Isaevaab, A. Bernadottebca

a National University of Science and Technology «MISIS», Moscow
b Sberbank
c Lomonosov Moscow State University
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Abstract: A binary classifier based on a convolutional and recurrent neural network, showed accuracy equal to 60% on average, with a maximum value of 78.9% when classifying EEG data from people who have undergone SARS-CoV-2 (COVID-19) and people who did not meet the SARS criteria. The data obtained support the hypothesis about the presence of the brain electrical activity patterns in people who have undergone SARS-CoV-2 (COVID-19).
Keywords: COVID-19, EEG, neural network, SARS-CoV-2.
Document Type: Article
Language: Russian
Citation: A. Zubov, M. Isaeva, A. Bernadotte, “Neural network classifier for EEG data from people who have undergone COVID-19 and have not”, Intelligent systems. Theory and applications, 25:4 (2021), 318–321
Citation in format AMSBIB
\Bibitem{ZubIsaBer21}
\by A.~Zubov, M.~Isaeva, A.~Bernadotte
\paper Neural network classifier for EEG data from people who have undergone COVID-19 and have not
\jour Intelligent systems. Theory and applications
\yr 2021
\vol 25
\issue 4
\pages 318--321
\mathnet{http://mi.mathnet.ru/ista472}
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  • https://www.mathnet.ru/eng/ista/v25/i4/p318
    • This publication is cited in the following 2 articles:
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