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. The 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.