Parvez, Mohammad ZavidDutta, ParipurnaZahin, FarhanAoyon, Omar MinhazDas, Aurindya2025-10-202025-10-2020202020-10ID 19301273ID 19301272ID 16301192ID 19201129http://hdl.handle.net/10361/26978Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-47).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.Epilepsy is a neurological disorder that causes serious seizure attacks. Effect of epileptic seizure is now so severe in world that it causes patients lifetime phenomenon which can be hindrance to people to get back to normal life again. Electroencephalogram (EEG) signals help to understand brain's activity more insight-fully. These EEG data recorded from normal and seizure patient to understand the brain activity. For classifying ictal and interictal period most of the classifier do not give 100 percent accuracy. In this paper, EEG signals has been used to analysis the distribution time-frequency features than calculated energy feature from it using Fourier transform methods. Beamforming methods using as noise distortion to have less noise to get more accuracy. The features are used for training data is in machine learning. Support vector machine is used as a classifier and this approach give us 89.8 percent accuracy for detecting epileptic seizures.51 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.EpilepsyEpileptic seizureSeizure attacksEEG signalsEEG data processingFourier transform methodsMachine learningDeep learning (Machine learning).Epilepsy--Diagnosis.Epilepsy--Prevention.Convulsions--Forecasting--Mathematical models.Fourier analysis.Epilepsy--Research--Mathematical models.Thesis