Comparison of EEG signal statistical parameters between healthy and SARS-CoV-2 affected individuals

This paper proposes a methodology for selecting and analyzing electroencephalographic (EEG) signals to compare patients with neurological changes due to SARS-CoV-2 infection with healthy individuals. The processing approach involves multiple steps, including windowing, filtering, and frequency analysis, all applied to the EEG data. These methods ensure a clear distinction between healthy and affected brain activity. After processing, key statistical parameters are extracted, averaged, and visualized to highlight the differences between the two groups. Specifically, skewness, kurtosis, and dominant frequency show notable variations. Skewness measures the asymmetry of the signal, kurtosis reflects the sharpness of the peaks, and dominant frequency captures the most prominent oscillations in the brain's activity. The analysis reveals that these parameters significantly differ between healthy individuals and those with neurological changes due to the virus. These differences can provide insights into the neurological impacts of SARS-CoV-2, offering a potential basis for further diagnosis and monitoring. Overall, the proposed methodology presents a systematic way to understand and compare the brain function of affected individuals against healthy controls.