Multiple sparse priors technique with optimized patches for brain source localization

Localizing brain neural activity using electroencephalography (EEG) neuroimaging technique is getting increasing response from neuroscience researchers and medical community. It is due to the fact that brain source localization has a variety of applications for diagnoses of various brain disorders....

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Main Authors: Jatoi, M.A., Kamel, N., López, J.D.
Format: Article
Published: John Wiley and Sons Inc. 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074298485&doi=10.1002%2fima.22370&partnerID=40&md5=3f805530b7c959854e9387d1b590dc8b
http://eprints.utp.edu.my/23316/
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spelling my.utp.eprints.233162021-08-19T07:25:37Z Multiple sparse priors technique with optimized patches for brain source localization Jatoi, M.A. Kamel, N. López, J.D. Localizing brain neural activity using electroencephalography (EEG) neuroimaging technique is getting increasing response from neuroscience researchers and medical community. It is due to the fact that brain source localization has a variety of applications for diagnoses of various brain disorders. This problem is ill-posed in nature because an infinite number of source configurations can produce the same potential at the head surface. Recently, a new technique that is based on Bayesian framework, called the multiple sparse priors (MSP), was proposed as a solution to this problem. The MSP develops the solution for source localization using the current densities associated with dipoles in terms of prior source covariance matrix and sensor covariance matrix, respectively. Then, it uses the maximization of the cost function of the free energy under the assumption of a fixed number of hyperparameters or patches in order to obtain the elements of prior source covariance matrix. This research work aims to further enhance the maximization process of MSP with regard to the free energy by considering a variable number of patches. This will lead to a better estimation of brain sources in terms of localization errors. The performance of the modified MSP with a variable number of patches is compared with the original MSP using simulated and real-time EEG data. The results show a significant improvement in terms of localization errors. © 2019 Wiley Periodicals, Inc. John Wiley and Sons Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074298485&doi=10.1002%2fima.22370&partnerID=40&md5=3f805530b7c959854e9387d1b590dc8b Jatoi, M.A. and Kamel, N. and López, J.D. (2020) Multiple sparse priors technique with optimized patches for brain source localization. International Journal of Imaging Systems and Technology, 30 (1). pp. 154-167. http://eprints.utp.edu.my/23316/
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 Localizing brain neural activity using electroencephalography (EEG) neuroimaging technique is getting increasing response from neuroscience researchers and medical community. It is due to the fact that brain source localization has a variety of applications for diagnoses of various brain disorders. This problem is ill-posed in nature because an infinite number of source configurations can produce the same potential at the head surface. Recently, a new technique that is based on Bayesian framework, called the multiple sparse priors (MSP), was proposed as a solution to this problem. The MSP develops the solution for source localization using the current densities associated with dipoles in terms of prior source covariance matrix and sensor covariance matrix, respectively. Then, it uses the maximization of the cost function of the free energy under the assumption of a fixed number of hyperparameters or patches in order to obtain the elements of prior source covariance matrix. This research work aims to further enhance the maximization process of MSP with regard to the free energy by considering a variable number of patches. This will lead to a better estimation of brain sources in terms of localization errors. The performance of the modified MSP with a variable number of patches is compared with the original MSP using simulated and real-time EEG data. The results show a significant improvement in terms of localization errors. © 2019 Wiley Periodicals, Inc.
format Article
author Jatoi, M.A.
Kamel, N.
López, J.D.
spellingShingle Jatoi, M.A.
Kamel, N.
López, J.D.
Multiple sparse priors technique with optimized patches for brain source localization
author_facet Jatoi, M.A.
Kamel, N.
López, J.D.
author_sort Jatoi, M.A.
title Multiple sparse priors technique with optimized patches for brain source localization
title_short Multiple sparse priors technique with optimized patches for brain source localization
title_full Multiple sparse priors technique with optimized patches for brain source localization
title_fullStr Multiple sparse priors technique with optimized patches for brain source localization
title_full_unstemmed Multiple sparse priors technique with optimized patches for brain source localization
title_sort multiple sparse priors technique with optimized patches for brain source localization
publisher John Wiley and Sons Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074298485&doi=10.1002%2fima.22370&partnerID=40&md5=3f805530b7c959854e9387d1b590dc8b
http://eprints.utp.edu.my/23316/
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