Chakrabarty, AmitabhaSaleque, ShahriarSpriha, Gul-A-ZannatKamal, MD Rasheeq IshraqKhan, Rafia Tabassum2021-07-032021-07-0320202020-04ID 19341016ID 16301089ID 16101074ID 16301081http://hdl.handle.net/10361/14724Cataloged from PDF version of thesis.Includes bibliographical references (pages 54-61).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.Depression is accorded as one of the leading causes to all the problems related to mental health in the Global disease burden study (GBD). Major depressive disorder (MDD) is when this depression reaches to a larger extent, when depression persists for two weeks or more. Sadly, many individuals of our society tend to neglect depression and refuse to label it as a mental disease and has a tendency to not seek medical help. Not only this, they are being curbed because of the few or very limited biological indicators for MDD and depression identification. Our main objective is to develop a non-intrusive approach that will detect and differentiate brain signals of patients with MDD from healthy patients. We were able to obtain an optimized model with an accuracy of (82%). Primarily we obtained a raw EEG data-set upon research and since it matched our requirements, we performed noise removal on them. Afterwards we extracted relevant features for depression detection, such as one feature was Absolute delta power. Finally, we entered these features into three classification algorithms; Logistic Regression (LR), Support Vector Machine (SVM) and Negative-Bayes (NB). To check the accuracy and precision, we performed a ten-fold cross validation on them. Hopefully, our results will encourage and motivate people suffering from this to seek the proper and effective medical help and to eradicate the negative stigma around it.61 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.MDDAbsolute delta powerLRSVMNBEEGMachine learning.Image processing.Thesis