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Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach

BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms o...

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Main Authors: Ahmad, R.F., Malik, A.S., Kamel, N., Reza, F., Amin, H.U., Hussain, M.
Format: Article
Published: IOS Press 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020233667&doi=10.3233%2fTHC-161286&partnerID=40&md5=809007937b381c8f82bc6f85273fdac3
http://eprints.utp.edu.my/19865/
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spelling my.utp.eprints.198652018-04-22T13:11:46Z Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach Ahmad, R.F. Malik, A.S. Kamel, N. Reza, F. Amin, H.U. Hussain, M. BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns. © 2017-IOS Press and the authors. All rights reserved. IOS Press 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020233667&doi=10.3233%2fTHC-161286&partnerID=40&md5=809007937b381c8f82bc6f85273fdac3 Ahmad, R.F. and Malik, A.S. and Kamel, N. and Reza, F. and Amin, H.U. and Hussain, M. (2017) Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach. Technology and Health Care, 25 (3). pp. 471-485. http://eprints.utp.edu.my/19865/
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 BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns. © 2017-IOS Press and the authors. All rights reserved.
format Article
author Ahmad, R.F.
Malik, A.S.
Kamel, N.
Reza, F.
Amin, H.U.
Hussain, M.
spellingShingle Ahmad, R.F.
Malik, A.S.
Kamel, N.
Reza, F.
Amin, H.U.
Hussain, M.
Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach
author_facet Ahmad, R.F.
Malik, A.S.
Kamel, N.
Reza, F.
Amin, H.U.
Hussain, M.
author_sort Ahmad, R.F.
title Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach
title_short Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach
title_full Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach
title_fullStr Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach
title_full_unstemmed Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach
title_sort visual brain activity patterns classification with simultaneous eeg-fmri: a multimodal approach
publisher IOS Press
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020233667&doi=10.3233%2fTHC-161286&partnerID=40&md5=809007937b381c8f82bc6f85273fdac3
http://eprints.utp.edu.my/19865/
_version_ 1738656130323709952
score 13.211869

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