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Proceedings Paper

Source counting in MEG neuroimaging
Author(s): Tianhu Lei; John Dell; Raphy Magee; Timothy P. L. Roberts
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Paper Abstract

Magnetoencephalography (MEG) is a multi-channel, functional imaging technique. It measures the magnetic field produced by the primary electric currents inside the brain via a sensor array composed of a large number of superconducting quantum interference devices. The measurements are then used to estimate the locations, strengths, and orientations of these electric currents. This magnetic source imaging technique encompasses a great variety of signal processing and modeling techniques which include Inverse problem, MUltiple SIgnal Classification (MUSIC), Beamforming (BF), and Independent Component Analysis (ICA) method. A key problem with Inverse problem, MUSIC and ICA methods is that the number of sources must be detected a priori. Although BF method scans the source space on a point-to-point basis, the selection of peaks as sources, however, is finally made by subjective thresholding. In practice expert data analysts often select results based on physiological plausibility. This paper presents an eigenstructure approach for the source number detection in MEG neuroimaging. By sorting eigenvalues of the estimated covariance matrix of the acquired MEG data, the measured data space is partitioned into the signal and noise subspaces. The partition is implemented by utilizing information theoretic criteria. The order of the signal subspace gives an estimate of the number of sources. The approach does not refer to any model or hypothesis, hence, is an entirely data-led operation. It possesses clear physical interpretation and efficient computation procedure. The theoretical derivation of this method and the results obtained by using the real MEG data are included to demonstrates their agreement and the promise of the proposed approach.

Paper Details

Date Published: 27 February 2009
PDF: 12 pages
Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 726223 (27 February 2009); doi: 10.1117/12.813655
Show Author Affiliations
Tianhu Lei, Children's Hospital of Philadelphia (United States)
Univ. of Pennsylvania (United States)
John Dell, Children's Hospital of Philadelphia (United States)
Univ. of Pennsylvania (United States)
Raphy Magee, Children's Hospital of Philadelphia (United States)
Univ. of Pennsylvania (United States)
Timothy P. L. Roberts, Children's Hospital of Philadelphia (United States)
Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 7262:
Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
Xiaoping P. Hu; Anne V. Clough, Editor(s)

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