Real-Time MEG Source Localization Using Regional Clustering.

Abstract:

:With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject's reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.

journal_name

Brain Topogr

journal_title

Brain topography

authors

Dinh C,Strohmeier D,Luessi M,Güllmar D,Baumgarten D,Haueisen J,Hämäläinen MS

doi

10.1007/s10548-015-0431-9

subject

Has Abstract

pub_date

2015-11-01 00:00:00

pages

771-84

issue

6

eissn

0896-0267

issn

1573-6792

pii

10.1007/s10548-015-0431-9

journal_volume

28

pub_type

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