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Estimating the Number of Sources in Magnetoencephalography Using Spiked Population Eigenvalues
Department of Statistics and Applied Probability, National University of Singapore, Singapore .
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-4023-6352
KLASMOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
Department of Statistics, Carnegie Mellon University, Pittsburgh Pennsylvania, USA.
2017 (English)In: Journal of the American Statistical Association, ISSN 0162-1459, E-ISSN 1537-274XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Magnetoencephalography (MEG) is an advanced imaging technique used to measure the magnetic fields outside the human head produced by the electrical activity inside the brain. Various source localization methods in MEG require the knowledge of the underlying active sources, which are identified by a priori. Common methods used to estimate the number of sources include principal component analysis or information criterion methods, both of which make use of the eigenvalue distribution of the data, thus avoiding solving the time-consuming inverse problem. Unfortunately, all these methods are very sensitive to the signal-to-noise ratio (SNR), as examining the sample extreme eigenvalues does not necessarily reflect the perturbation of the population ones. To uncover the unknown sources from the very noisy MEG data, we introduce a framework, referred to as the intrinsic dimensionality (ID) of the optimal transformation for the SNR rescaling functional. It is defined as the number of the spiked population eigenvalues of the associated transformed data matrix. It is shown that the ID yields a more reasonable estimate for the number of sources than its sample counterparts, especially when the SNR is small. By means of examples, we illustrate that the new method is able to capture the number of signal sources in MEG that can escape PCA or other information criterion based methods.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2017.
Keyword [en]
Brain imaging, inverse MEG problem, spiked eigenvalues, intrinsic dimensionality, eigenthresholding
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-58692DOI: 10.1080/01621459.2017.1341411OAI: oai:DiVA.org:oru-58692DiVA: diva2:1127585
Available from: 2017-07-17 Created: 2017-07-17 Last updated: 2017-10-18Bibliographically approved

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Zhang, Ye

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CiteExportLink to record
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