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 Topogrjournal_title
Brain topographyauthors
Dinh C,Strohmeier D,Luessi M,Güllmar D,Baumgarten D,Haueisen J,Hämäläinen MSdoi
10.1007/s10548-015-0431-9subject
Has Abstractpub_date
2015-11-01 00:00:00pages
771-84issue
6eissn
0896-0267issn
1573-6792pii
10.1007/s10548-015-0431-9journal_volume
28pub_type
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