Object categories specific brain activity classification with simultaneous EEG-fMRI

Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities t...

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Main Authors: Ahmad, R.F., Malik, A.S., Kamel, N., Reza, F.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2015
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953248170&doi=10.1109%2fEMBC.2015.7318735&partnerID=40&md5=3a8929c8f1fd4578d588d6e3e5c82d29
http://eprints.utp.edu.my/26197/
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Institution: Universiti Teknologi Petronas
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spelling my.utp.eprints.261972021-08-30T08:54:18Z Object categories specific brain activity classification with simultaneous EEG-fMRI Ahmad, R.F. Malik, A.S. Kamel, N. Reza, F. Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8 as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature. © 2015 IEEE. Institute of Electrical and Electronics Engineers Inc. 2015 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953248170&doi=10.1109%2fEMBC.2015.7318735&partnerID=40&md5=3a8929c8f1fd4578d588d6e3e5c82d29 Ahmad, R.F. and Malik, A.S. and Kamel, N. and Reza, F. (2015) Object categories specific brain activity classification with simultaneous EEG-fMRI. In: UNSPECIFIED. http://eprints.utp.edu.my/26197/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8 as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature. © 2015 IEEE.
format Conference or Workshop Item
author Ahmad, R.F.
Malik, A.S.
Kamel, N.
Reza, F.
spellingShingle Ahmad, R.F.
Malik, A.S.
Kamel, N.
Reza, F.
Object categories specific brain activity classification with simultaneous EEG-fMRI
author_facet Ahmad, R.F.
Malik, A.S.
Kamel, N.
Reza, F.
author_sort Ahmad, R.F.
title Object categories specific brain activity classification with simultaneous EEG-fMRI
title_short Object categories specific brain activity classification with simultaneous EEG-fMRI
title_full Object categories specific brain activity classification with simultaneous EEG-fMRI
title_fullStr Object categories specific brain activity classification with simultaneous EEG-fMRI
title_full_unstemmed Object categories specific brain activity classification with simultaneous EEG-fMRI
title_sort object categories specific brain activity classification with simultaneous eeg-fmri
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953248170&doi=10.1109%2fEMBC.2015.7318735&partnerID=40&md5=3a8929c8f1fd4578d588d6e3e5c82d29
http://eprints.utp.edu.my/26197/
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