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Electroencephalography is an electrophysiological monitoring process to capture electrical activity on the scalp that has been shown to represent the macroscopic activity of the surface layer of the brain underneath. It is typically non-invasive, with the electrodes placed along the scalp. Computer programs in different programming language such as MATLAB, Python are used to simulate and study brain signals. This thesis focuses on utilizing Python, an open-source programming language to understand the impact of alcohol on one’s memory and attention and comparing them with non-alcoholic brain. To carry out this research, we are using open-source EEG data collected from alcoholic and non-alcoholic subjects subjected to visual stimuli. Experiments are carried out to observe spatial patterns related to both groups' brain activity and their association with different region of brain such as memory, attention, somatosensory, and emotional regulation regions. Besides the spatial pattern, we are also focusing to find source signals and their association with respect to attention region to understand the impact of alcohol on one’s attention function. Finally, the optimal sources based on optimal alpha and gamma rhythms are estimated. For these optimal source channels, we estimated time-frequency based spectrogram to understand the association of other band powers for both groups. Beta power activities from these spectrograms are analyzed for both groups to understand attention-deficit caused by alcohol consumption. By analyzing the results from the experiments can help us understand the impact of alcohol on one's brain's activity.