Source counting in MEG neuroimaging

Tianhu Lei; John Dell; Raphy Magee; Timothy P. L. Roberts

doi:10.1117/12.813655

27 February 2009 Source counting in MEG neuroimaging

Tianhu Lei, John Dell, Raphy Magee, Timothy P. L. Roberts

Author Affiliations +

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 726223 (2009) https://doi.org/10.1117/12.813655
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Tianhu Lei, John Dell, Raphy Magee, and Timothy P. L. Roberts "Source counting in MEG neuroimaging", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 726223 (27 February 2009); https://doi.org/10.1117/12.813655

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KEYWORDS

Magnetoencephalography

Sensors

Magnetism

Independent component analysis

Data acquisition

Interference (communication)

Inverse problems

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