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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 17 - 2023 | doi: 10.3389/fnins.2023.1019778

Investigating brain cortical activity in patients with post-COVID-19 brain fog

 Grzegorz M. Wójcik1*, Lukasz Kwasniewicz1, Andrzej Kawiak1,  Bernadetta Bartosik1,  Krzysztof Wrobel1, Oren Shriki2, Yarden Ben-Horin3, Sagi Furman3 and Ewelina Panas4
  • 1Department of Neuroinformatics and Biomedical Engineering, Marie Curie-Sklodowska University, Poland
  • 2Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Israel
  • 3Department of Computer Science, Ben-Gurion University of the Negev, Israel
  • 4Department of International Relations, Marie Curie-Sklodowska University, Poland
Provisionally accepted:
The final, formatted version of the article will be published soon.

Brain fog is a kind of mental problem, similar to Chronic Fatigue Syndrome and it appears about three months after the infection with COVID-19 and lasts up to nine months. The maximum magnitude of the 3rd wave of COVID-19 in Poland was in April 2021.
The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B) versus the control group that had no COVID-19 and no symptoms (sub-cohort C).
The aim of this paper was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and if possible to differentiate and classify them using the machine learning tools.
The dense array electroencephalographic amplifier with 256 electrodes was used for recordings.
The Event-Related Potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: Face Recognition, Digit Span and Task Switching. These potentials were plotted for all three patients sub-cohorts and all three experiments.
The cross-correlation method was used to find differences and in fact, such differences manifested themselves in the shape of Event-Related Potentials on the cognitive electrodes. The discussion of such differences will be presented to some extent.
In the classification problem there were used the the avalanche analysis for feature extractions from the resting state signal that was also collected and Linear Discriminant Analysis for classification. The differences between sub-cohorts in such signal were expected to be found. Machine learning tools were used as finding the differences with the eye seemed to be impossible. Indeed, the A&B vs C, B&C vs A, A vs B, A vs C, B vs C classification tasks were performed and the efficiency of around 60%-70% was achieved.

Keywords: brain fog, EEG, ERP, COVID-19, cortical activity, LDA

Received:15 Aug 2022; Accepted: 12 Jan 2023.

Copyright: © 2023 Wójcik, Kwasniewicz, Kawiak, Bartosik, Wrobel, Shriki, Ben-Horin, Furman and Panas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Grzegorz M. Wójcik, Marie Curie-Sklodowska University, Department of Neuroinformatics and Biomedical Engineering, Lublin, Poland