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Formation of Channel Correlation Based Significant Virtual Images from EEG Sub-bands Data to Recognize Human Emotion using Convolutional Neural Network

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dc.contributor.advisor Ahmad, Prof. Dr. Mohiuddin
dc.contributor.author Islam, Md. Rabiul
dc.date.accessioned 2019-09-26T10:01:45Z
dc.date.available 2019-09-26T10:01:45Z
dc.date.copyright 2019
dc.date.issued 2019-07
dc.identifier.other ID 1603557
dc.identifier.uri http://hdl.handle.net/20.500.12228/534
dc.description This thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, July 2019. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 85-91).
dc.description.abstract Emotions are the most fundamental feature for non-verbal communication between human and machine. But the improvement of different human interacted media depends largely on the appropriate recognition of the human feeling that means what he or she wants just right at that moment. To communicate computer with human, to detect present feelings of the patient, to understand customer interest for shopping, to realize interest of physically handicapped people or to detect the lie of a convict; emotion recognition is a fundamental prerequisite. But due to some complexities, the proper recognition of human emotion from Electroencephalogram (EEG) has become too much challenging. Normally, feature-based emotion recognition requires a strong effort to design the perfect feature or feature set related to the classification of emotion. To curtail the manual human effort of feature extraction, we designed a model with Convolutional Neural Network (CNN). As Electroencephalogram (EEG) is 1D data, to use CNN the 1D EEG data have to be converted into 2D significant image data. That is a challenging task. To meet up this challenge, initially, we calculated Pearson’s correlation coefficients form different sub-bands of EEG to formulate a virtual image. Later, this virtual image was fed into a CNN architecture to classify emotion. We made two distinct protocols; between these, protocol-1 was to classify positive and negative emotion and protocol-2 was to classify three distinct emotions. Overall maximum accuracy of 76.52% on valence and 76.82% on arousal was obtained by using internationally authorized DEAP dataset. We observed that the Convolutional Neural Network (CNN) based method showed state-of-the-art performance for emotion classification. en_US
dc.description.statementofresponsibility Md. Hasibul Hasan
dc.format.extent 91 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh en_US
dc.rights Khulna University of Engineering & Technology (KUET) thesis/dissertation/internship reports 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.subject Human Emotion en_US
dc.subject Electroencephalogram (EEG) en_US
dc.subject Virtual Image en_US
dc.subject Deep Architecture en_US
dc.title Formation of Channel Correlation Based Significant Virtual Images from EEG Sub-bands Data to Recognize Human Emotion using Convolutional Neural Network en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Electrical and Electronic Engineering
dc.contributor.department Department of Electrical and Electronic Engineering


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