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dc.contributor.advisorParvez, Mohammad Zavid
dc.contributor.advisorAbrar, Mohammed Abid
dc.contributor.authorRiduan, Jonayed Ahmed
dc.contributor.authorMahjabin, Most.
dc.contributor.authorMim, Nadia Tasnim
dc.contributor.authorIslam, Ridwane-ul
dc.contributor.authorRana, Md. Shahriar Rahman
dc.date.accessioned2021-10-07T03:30:23Z
dc.date.available2021-10-07T03:30:23Z
dc.date.copyright2021
dc.date.issued2021-01
dc.identifier.otherID 17101028
dc.identifier.otherID 17101187
dc.identifier.otherID 17101487
dc.identifier.otherID 17101495
dc.identifier.otherID 18201195
dc.identifier.urihttp://hdl.handle.net/10361/15160
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (page 35-39).
dc.description.abstractEmotion can be defined as the neurophysiological changes people experience due to significant internal or external occasions. This is a mental condition that can a↵ect a person’s behavior, mood, way of life, and relationship with others. As it directly a↵ects one’s life, emotion recognition is an important subject in the area of research field. In recent years, there has been a relentless e↵ort to develop several models and datasets to detect human emotions and analyze them to understand the depth of complex human feelings and reduce error in the detection of emotions. To get better results in recognizing emotion, extensive research is needed to be done on the feature extraction methods and channel selection. While measuring the performance of di↵erent classification algorithms, it is very important to compare the results as well as preprocessing techniques. In this work, we extracted DWT wavelet features of the EEG channels from the DEAP dataset and used a statistical parameter Root Sum Square (RSS) to reduce the dimension of the features. Then we applied a channel selection algorithm on the preprocessed EEG data and selected ten channels with the highest average power. Finally, we classified positive and negative emotion related to valence and arousal using di↵erent classification algorithms (like KNN, RF, Bagging, Extra Tree, AdaBoost and MLP) for the selected EEG channels as well as for all EEG channels. The accuracy reports achieved for the selected channels were impressive; the highest test accuracy 67.58% for Valence was retrieved from the Bagging and Extra Trees classifier while MLP achieved the highest test accuracy result 63.67% for Arousal.en_US
dc.description.statementofresponsibilityJonayed Ahmed Riduan
dc.description.statementofresponsibilityMost. Mahjabin
dc.description.statementofresponsibilityNadia Tasnim Mim
dc.description.statementofresponsibilityRidwane-ul-Islam
dc.description.statementofresponsibilityMd. Shahriar Rahman Rana
dc.format.extent39 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectEmotion recognitionen_US
dc.subjectDEAPen_US
dc.subjectEEGen_US
dc.subjectChannel Selectionen_US
dc.subjectDWTen_US
dc.subjectRSSen_US
dc.subjectPSDen_US
dc.subjectKNNen_US
dc.subjectRFen_US
dc.subjectBaggingen_US
dc.subjectExtra Treeen_US
dc.subjectAdaBoosten_US
dc.subjectMLPen_US
dc.subject.lcshEmotion recognition
dc.titleA comparative analysis of emotion recognition using EEG signals with a channel selection techniqueen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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