Neural network classifier for EEG data from people who have undergone COVID-19 and have not

A binary classifier based on a convolutional and recurrent neural network, showed accuracy equal to 60% on average, with a maximum value of 78.9% when classifying EEG data from people who have undergone SARS-CoV-2 (COVID-19) and people who did not meet the SARS criteria. The data obtained support the hypothesis about the presence of the brain electrical activity patterns in people who have undergone SARS-CoV-2 (COVID-19).