Frequency, time, and spatial EEG-changes after COVID-19 during a simple speech task.
Using data analysis and indirect application of neural networks in our work, we identified patterns of brain electrical activity that characterize COVID−19. We were interested in frequency, temporal, and spatial domain patterns of electrical activity in people who have undergone COVID−19.
We found a predominance of α−rhythm patterns in the left hemisphere in healthy people compared to people who have had COVID−19. Moreover, we observe a significant decrease in the left hemisphere contribution to the speech center area in 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 hemispheres when performing tasks compared to people who recovered from COVID−19. We also observed a decrease in low frequencies in both hemispheres after COVID−19.
EEG-patterns of COVID−19 are detectable in an unusual frequency domain. What is usually considered noise in EEG-data carries information that can be used to determine whether or not a person has had COVID−19. These patterns can be interpreted as signs of hemispheric desynchronization, premature brain aging, and more significant 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−19 on EEG−data. Furthermore, our data in accordance with other studies supported the hypothesis of the severity of the long-term effects of 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 to detect post-COVID syndrome and develop progress in neurorehabilitation.