eprintid: 5895
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/58/95
datestamp: 2023-11-09 16:17:38
lastmod: 2023-11-09 16:17:38
status_changed: 2023-11-09 16:04:11
type: conference_item
metadata_visibility: show
creators_name: Rasheed, W.
creators_name: Tang, T.B.
creators_name: Bin Hamid, N.H.
title: Default mode functional connectivity estimation and visualization framework for MEG data
ispublished: pub
keywords: Brain; Brain mapping; Electroencephalography; Electrophysiology; Magnetoencephalography; Sensor data fusion; Visualization, Brain regions; Functional connectivity; Healthy subjects; Position and orientations; Sensor positioning; Temporal resolution; Visualization and analysis; Visualization framework, Data visualization
note: cited By 0; Conference of 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 ; Conference Date: 22 April 2015 Through 24 April 2015; Conference Code:113593
abstract: 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.
date: 2015
publisher: IEEE Computer Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940388741&doi=10.1109%2fNER.2015.7146824&partnerID=40&md5=70116f8fe0ef76b121b8d19da2804a3d
id_number: 10.1109/NER.2015.7146824
full_text_status: none
publication: International IEEE/EMBS Conference on Neural Engineering, NER
volume: 2015-J
pagerange: 1116-1119
refereed: TRUE
isbn: 9781467363891
issn: 19483546
citation:   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.