Visualizing various Electroencephalogram signal components in case of different brain conditions

Electroencephalogram (EEG) signals, renowned for their ability to unveil the dynamic workings of the human brain, serve as an invaluable resource in deciphering an array of neurological conditions. This research endeavors to conduct an exhaustive visual analysis of EEG signal components, employing advanced signal processing techniques and visualization methodologies. Our primary aim is to delineate the distinct patterns of brain wave activity associated with a spectrum of neurological conditions, encompassing ’Healthy control’, ’Depressive disorder’, ’Bipolar disorder’, ’Panic disorder’, ’Social anxiety disorder’, ’Alcohol use disorder’, ’Behavioral addiction disorder’, ’Post Ttraumatic Stress Disorder’ (PTSD), ’Acute stress disorder’(ASD), and ’Adjustment disorder’. Through meticulous examination of these visual representations, our goal is to deepen our understanding of the underlying neurophysiological mechanisms characterizing these conditions. Although it is very difficult to visualize the EEG signals, even of the already preprocessed EEG signals, but still the paper visualizes diverse brain conditions according to the lobes of the brain. Different frequency components affect the different brain lobes in different conditions. We have used Minimum Norm Estimation (MNE) library of Python, which is designed exclusively for visualizing various Magnetoencephalogram (MEG) and Electroencephalogram (EEG) Signals of the brain.