Parvez, Mohammad ZavidIslam, Sayedi Hassan BinMehdi, Md. QuamarRohan, Bhuiyan YashMahmood, Syed Atif Imtiaz2019-10-142019-10-14201920192019-08ID 19341036ID 19141036ID 19341031ID 14201015http://hdl.handle.net/10361/12782Cataloged from PDF version of thesis.Includes bibliographical references (pages 35-46).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.Emotion is a mental state, which originates in the brain and is closely related to the nervous system. Emotion can be defined as a feeling expressed through, or detectable by voice intonation, facial expression body language, as response from one’s mood relationship with others and most importantly the circumstance they are in. Although, Brain Computer Interface (BCI) are being developed to find a better human-machine interaction system using brain activity and it is frequently implemented by Electroencephalogram (EEG) signals. EEG is a well established approach to measure the brain activities which can be analyzed and processed to distinguish different emotions. In this thesis, we present an approach to classify human emotions using EEG signal by Convolutional Neural Network(CNN). In our model, we use the Dataset for Emotion Analysis using Physiological signals (DEAP) dataset, a benchmark for emotion classification research, to transform the EEG signal from time domain to frequency domain and extract the features to classify the emotions. Emotion can be classified based on the two dimensions of valence and arousal. Previous researches have used fewer channels and participants. Our approach which was carried out on 32 participants, has achieved an accuracy of 94.75% for the valence and 95.75% on the arousal detection, which is quite competitive with other methods of emotion recognition.46 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.EEGBCICNNFFTDCTDWTEmotions--Computer simulationPattern recognition systemsArtificial intelligenceHuman-computer interactionThesis