Depression is a mental illness that is associated with feelings of sadness and hopelessness. A person's mental and physical health are also impacted. Also, it is challenging to identify depression since there are currently no established diagnostic procedures for the condition that may yield definitive results. Many depressed individuals are completely ignorant of their condition. The electrochemical potential of the brain may be changed, and this change can be detected by electroencephalographic (EEG) data. The automatic categorization of the normal as well as depressive EEG signals is the foundation of the current study. In order to retrieve hidden information from the EEG data, signal processing techniques are required.In this study, the pre-processing, feature extraction, along with classification procedures are used to distinguish between normal and depressive EEG data. The system's main goal is to increase the precision of patient monitoring systems, remove variations in EEG signals, and enhance process accuracy.

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