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Proceedings Paper

Advances in electromagnetic brain imaging
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Paper Abstract

Non-invasive and dynamic imaging of brain activity in the sub-millisecond time-scale is enabled by measurements on or near the scalp surface using an array of sensors that measure magnetic fields (magnetoencephalography (MEG)) or electric potentials (electroencephalography (EEG)). Algorithmic reconstruction of brain activity from MEG and EEG data is referred to as electromagnetic brain imaging (EBI). Reconstructing the actual brain response to external events and distinguishing unrelated brain activity has been a challenge for many existing algorithms in this field. Furthermore, even under conditions where there is very little interference, accurately determining the spatial locations and timing of brain sources from MEG and EEG data is challenging problem because it involves solving for unknown brain activity across thousands of voxels from just a few sensors (~300). In recent years, my research group has developed a suite of novel and powerful algorithms for EBI that we have shown to be considerably superior to existing benchmark algorithms. Specifically, these algorithms can solve for many brain sources, including sources located far from the sensors, in the presence of large interference from unrelated brain sources. Our algorithms efficiently model interference contributions to sensors, accurately estimate sparse brain source activity using fast and robust probabilistic inference techniques. Here, we review some of these algorithms and illustrate their performance in simulations and real MEG/EEG data.

Paper Details

Date Published: 17 February 2010
PDF: 14 pages
Proc. SPIE 7527, Human Vision and Electronic Imaging XV, 752709 (17 February 2010); doi: 10.1117/12.849117
Show Author Affiliations
Srikantan S. Nagarajan, Univ. of California, San Francisco (United States)

Published in SPIE Proceedings Vol. 7527:
Human Vision and Electronic Imaging XV
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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