Analysis on Detection of Chronic Alcoholics from EEG Signal Segments—A Comparative Study Between Two Software Tools

Alcohol consumption is vulnerable to the brain and has a high risk of brain damage and other neurobehavioral deficits. This paper primarily focuses on massive data generated from EEG signals and its characterization with respect to various states of the human brain under influence of alcohol. A single trial 64-channel EEG database is utilized for classification of alcoholic states for a single patient. Singular Value Decomposition (SVD) features of EEG segments are computed. Even though EEG signals are acquired from alcoholic patient some of the EEG signal segments resemble EEG segments of normal, alcoholic, and epileptic persons. Depending on the SVD values, EEG segments are labeled as normal, alcoholic, and epileptic and then classified through Hard Thresholding and K-means clustering techniques. The classification is done using two different softwares in this paper, namely, MATLAB and R studio and then the results are compared. The results show that MATLAB software classifies better than R studio software with comparatively highest classification accuracy of 83.5% which is obtained when Hard Thresholding method is utilized.