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Default mode functional connectivity estimation and visualization framework for MEG data

Magnetoencephalography (MEG) is used for functional connectivity analysis, and can record brain signals from deep sources non-invasively. Modern MEG systems measure signals at a temporal resolution of milliseconds and at millimeter precision. However, there is a lack of standardization in the positi...

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Main Authors: Rasheed, W., Tang, T.B., Bin Hamid, N.H.
Format: Conference or Workshop Item
Published: IEEE Computer Society 2015
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940388741&doi=10.1109%2fNER.2015.7146824&partnerID=40&md5=70116f8fe0ef76b121b8d19da2804a3d
http://eprints.utp.edu.my/26280/
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spelling my.utp.eprints.262802021-08-30T07:06:31Z Default mode functional connectivity estimation and visualization framework for MEG data Rasheed, W. Tang, T.B. Bin Hamid, N.H. Magnetoencephalography (MEG) is used for functional connectivity analysis, and can record brain signals from deep sources non-invasively. Modern MEG systems measure signals at a temporal resolution of milliseconds and at millimeter precision. However, there is a lack of standardization in the position and orientation of sensors, unlike the electroencephalography (EEG) that follows sensor positioning guidelines defined by international 10-20 10-10 or 10-5 systems. Mapping MEG sensor positioning to EEG's is essential to enable data fusion and comparison of both modalities. This paper reports the development of a novel framework for MEG data visualization and analysis. The strength of the proposed framework is demonstrated through input of sizeable data from multiple healthy subjects and generating default mode connectivity visualization from the most common and significantly active coherent brain regions. © 2015 IEEE. IEEE Computer Society 2015 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940388741&doi=10.1109%2fNER.2015.7146824&partnerID=40&md5=70116f8fe0ef76b121b8d19da2804a3d Rasheed, W. and Tang, T.B. and Bin Hamid, N.H. (2015) Default mode functional connectivity estimation and visualization framework for MEG data. In: UNSPECIFIED. http://eprints.utp.edu.my/26280/
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 Magnetoencephalography (MEG) is used for functional connectivity analysis, and can record brain signals from deep sources non-invasively. Modern MEG systems measure signals at a temporal resolution of milliseconds and at millimeter precision. However, there is a lack of standardization in the position and orientation of sensors, unlike the electroencephalography (EEG) that follows sensor positioning guidelines defined by international 10-20 10-10 or 10-5 systems. Mapping MEG sensor positioning to EEG's is essential to enable data fusion and comparison of both modalities. This paper reports the development of a novel framework for MEG data visualization and analysis. The strength of the proposed framework is demonstrated through input of sizeable data from multiple healthy subjects and generating default mode connectivity visualization from the most common and significantly active coherent brain regions. © 2015 IEEE.
format Conference or Workshop Item
author Rasheed, W.
Tang, T.B.
Bin Hamid, N.H.
spellingShingle Rasheed, W.
Tang, T.B.
Bin Hamid, N.H.
Default mode functional connectivity estimation and visualization framework for MEG data
author_facet Rasheed, W.
Tang, T.B.
Bin Hamid, N.H.
author_sort Rasheed, W.
title Default mode functional connectivity estimation and visualization framework for MEG data
title_short Default mode functional connectivity estimation and visualization framework for MEG data
title_full Default mode functional connectivity estimation and visualization framework for MEG data
title_fullStr Default mode functional connectivity estimation and visualization framework for MEG data
title_full_unstemmed Default mode functional connectivity estimation and visualization framework for MEG data
title_sort default mode functional connectivity estimation and visualization framework for meg data
publisher IEEE Computer Society
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940388741&doi=10.1109%2fNER.2015.7146824&partnerID=40&md5=70116f8fe0ef76b121b8d19da2804a3d
http://eprints.utp.edu.my/26280/
_version_ 1738656849387847680
score 13.211869

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