Source Localization with MEG Data: A Beamforming Approach Based on Covariance Thresholding

Zhang, Jian and Liu, Chao and Green, Gary (2014) Source Localization with MEG Data: A Beamforming Approach Based on Covariance Thresholding. Biometrics, 70 (1). pp. 121-131. ISSN 1541-0420. (doi:https://doi.org/10.1111/biom.12123) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

Reconstructing neural activities using non-invasive sensor arrays outside the brain is an ill-posed inverse problem since the observed sensor measurements could result from an infinite number of possible neuronal sources. The sensor covariance-based beamformer mapping represents a popular and simple solution to the above problem. In this article, we propose a family of beamformers by using covariance thresholding. A general theory is developed on how their spatial and temporal dimensions determine their performance. Conditions are provided for the convergence rate of the associated beamformer estimation. The implications of the theory are illustrated by simulations and a real data analysis.

Item Type: Article
Uncontrolled keywords: Beamforming; covariance thresholding; MEG neuroimaging; source localization and reconstruction; varying coefficient models.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 07 Jul 2014 14:23 UTC
Last Modified: 06 Mar 2015 11:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41702 (The current URI for this page, for reference purposes)
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