Default mode functional connectivity estimation and visualization framework for MEG data

• 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/31417/

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.