Frequency, time, and spatial electroencephalogram changes after COVID-19 during a simple speech task

Vorontsova, D.V.; Isaeva, M.V.; Menshikov, I.A.; Orlov, K.Y.; Bernadotte, A.

Статья опубликована в журнале из списка RSCI Web of Science

Информация о цитировании статьи получена из Scopus
Статья опубликована в журнале из перечня ВАК
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 20 февраля 2024 г.

Авторы: Vorontsova Darya V., Isaeva Marina V., Menshikov Ivan A., Orlov Kirill Yu, Bernadotte Alexandra
Журнал: Компьютерные исследования и моделирование
Том: 15
Номер: 3
Год издания: 2023
Издательство: Автономная некоммерческая организация Ижевский институт компьютерных исследований
Местоположение издательства: Ижевск
Первая страница: 691
Последняя страница: 701
DOI: 10.20537/2076-7633-2023-15-3-691-701
Аннотация: We found a predominance of α-rhythm patterns in the left hemisphere in healthy people compared to people withCOVID-19 history. Moreover, we observe a significant decrease in the left hemisphere contribution to the speech center areain people who have undergone COVID-19 when performing speech tasks.Our findings show that the signal in healthy subjects is more spatially localized and synchronized between hemisphereswhen performing tasks compared to people who recovered from COVID-19. We also observed a decrease in low frequenciesin both hemispheres after COVID-19.EEG-patterns of COVID-19 are detectable in an unusual frequency domain. What is usually considered noise inelectroencephalographic (EEG) data carries information that can be used to determine whether or not a person has hadCOVID-19. These patterns can be interpreted as signs of hemispheric desynchronization, premature brain ageing, and moresignificant brain strain when performing simple tasks compared to people who did not have COVID-19.In our work, we have shown the applicability of neural networks in helping to detect the long-term effects of COVID-19on EEG-data. Furthermore, our data following other studies supported the hypothesis of the severity of the long-term effectsof COVID-19 detected on the EEG-data of EEG-based BCI. The presented findings of functional activity of the brain–computer interface make it possible to use machine learning methods on simple, non-invasive brain–computer interfaces todetect post-COVID syndrome and develop progress in neurorehabilitation.
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Frequency, time, and spatial electroencephalogram changes after COVID-19 during a simple speech taskстатья

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