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: | , , |
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| Format: | Conference or Workshop Item |
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IEEE Computer Society
2015
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| 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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| Summary: | 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. |
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