AUTHOR=Wojcik Grzegorz M. , Shriki Oren , Kwasniewicz Lukasz , Kawiak Andrzej , Ben-Horin Yarden , Furman Sagi , Wróbel Krzysztof , Bartosik Bernadetta , Panas Ewelina 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1019778

DOI=10.3389/fnins.2023.1019778

ISSN=1662-453X

ABSTRACT=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.