Visual object classification from fMRI data

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Detalles Bibliográficos
Main Authors:	Newaz, Syed Mishar, Taseeb, Taslim Ahmed, Haque, Abdullah Nurul
Outros autores:	Zavid Parvez, Mohammad
Formato:	Thesis
Idioma:	English
Publicado:	Brac University 2022
Subjects:	Functional MRI Visual features Convolutional neural network Deep learning Cognitive learning theory (Deep learning) Machine learning Neural networks (Computer science)
Acceso en liña:	http://hdl.handle.net/10361/16779

id	10361-16779
record_format	dspace
spelling	10361-167792022-06-01T21:03:49Z Visual object classification from fMRI data Newaz, Syed Mishar Taseeb, Taslim Ahmed Haque, Abdullah Nurul Zavid Parvez, Mohammad Department of Computer Science and Engineering, Brac University Functional MRI Visual features Convolutional neural network Deep learning Cognitive learning theory (Deep learning) Machine learning Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 19-20). Computing devices were once limited in just calculating arithmetic. Whereas, in modern computing, complex task like object classi cation or recognition has become so popular that even our smart devices cannot be thought without having a voice, character and face recognition features. Although it has been a long time since the idea of object recognition rst came into the scene, there has been limited amount of work done in categorising objects from human fMRI data. As a result, part of human cognitive study has been neglected which possesses a large potential to be discovered and used. In brief, when a human perceives an object through vision or imagination, certain regions of brain generate speci c patterns of electric signals. Using fMRI brain data, we can potentially use those signals to interpret whatever a person is perceiving. We have tried to recreate some of the few works done previously in a limited test environment. In this paper, we try to explore an approach where a random perceived object gets split into a bunch of features it possesses. Using those extracted features, we will be able to classify the object from our previously trained deep learning model. Finally, our experiment will show a robust approach to explore and study human cognition using computers. Syed Mishar Newaz Taslim Ahmed Taseeb Abdullah Nurul Haque B. Computer Science 2022-06-01T04:45:48Z 2022-06-01T04:45:48Z 2022 2022-01 Thesis ID 18101210 ID 18101443 ID 18101694 http://hdl.handle.net/10361/16779 en Brac 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. 20 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Functional MRI Visual features Convolutional neural network Deep learning Cognitive learning theory (Deep learning) Machine learning Neural networks (Computer science)
spellingShingle	Functional MRI Visual features Convolutional neural network Deep learning Cognitive learning theory (Deep learning) Machine learning Neural networks (Computer science) Newaz, Syed Mishar Taseeb, Taslim Ahmed Haque, Abdullah Nurul Visual object classification from fMRI data
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Zavid Parvez, Mohammad
author_facet	Zavid Parvez, Mohammad Newaz, Syed Mishar Taseeb, Taslim Ahmed Haque, Abdullah Nurul
format	Thesis
author	Newaz, Syed Mishar Taseeb, Taslim Ahmed Haque, Abdullah Nurul
author_sort	Newaz, Syed Mishar
title	Visual object classification from fMRI data
title_short	Visual object classification from fMRI data
title_full	Visual object classification from fMRI data
title_fullStr	Visual object classification from fMRI data
title_full_unstemmed	Visual object classification from fMRI data
title_sort	visual object classification from fmri data
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/16779
work_keys_str_mv	AT newazsyedmishar visualobjectclassificationfromfmridata AT taseebtaslimahmed visualobjectclassificationfromfmridata AT haqueabdullahnurul visualobjectclassificationfromfmridata
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Visual object classification from fMRI data

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