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

Zhang, Jian, Liu, Chao, 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: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

covariance-based beamformer mapping represents a popular and simple solution to the above problem. In this article, we

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
DOI/Identification number: 10.1111/biom.12123
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: 13 Feb 2020 04:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41702 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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