In recent times, there has been increasing interest in utilizing EEG-based techniques for studying Major Depressive Disorder as a dynamic method. Although it is frequently used for identifying depression, the method is still difficult to interpret. The conventional treatment of MDD involves medications such as Selective Serotonin Reuptake Inhibitors, which often have adverse effects. On the other hand, the use of dimethyltryptamine to stimulate brain activity in regions where MDD patients show lower activity has demonstrated promising results. This study analyzed resting-state EEG signals from MDD patients, DMT users, and healthy controls to evaluate and validated a computer-aided approach. The brain activity of DMT users was recorded and compared with MDD individuals and healthy controls. Using Welch's method, the power of several frequency bands was analyzed from the EEG dataset for comparison and diagnosis. The extracted EEG data underwent noise removal and feature extraction. The features from all controls were concatenated to form a data matrix. Furthermore, the data matrix was standardized using the Z-score standardization method. The classifier model logistic regression was employed to train and test the extracted features. The results of the investigations have demonstrated the most important features, such as signal power of the EEG data from the frontal, temporal, parietal, and occipital brain areas, to be significant.