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On Linearly Constrained Minimum Variance Beamforming

Zhang, Jian, Liu, Chao (2015) On Linearly Constrained Minimum Variance Beamforming. Journal of Machine Learning Research, 16 (1). ISSN 1532-4435. E-ISSN 1533-7928. (doi:10.5555/2789272.2886818)

Abstract

Beamforming is a widely used technique for source localization in signal processing and neuroimaging. A number of vector-beamformers have been introduced to localize neuronal activity by using

on these beamformers have been limited to simple cases, where no more than two sources are allowed in the associated model and the theoretical sensor covariance

In the present study, we consider a class of vector-beamformers defined by thresholding the sensor covariance matrix, which include the standard vector-beamformer as a special case.

these vector-beamformers, which shows the extent of effects to which the MEG spatial and temporal dimensions on estimating the neuronal activity index. The performances of the proposed beamformers are assessed by simulation studies. Superior performances of the proposed beamformers are obtained

We apply the proposed procedure to real MEG datasets derived from five sessions of a human face-perception experiment, finding several highly active areas in the brain. A good agreement between these findings and the known neurophysiology of the MEG response to human face perception is shown.

Item Type: Article
DOI/Identification number: 10.5555/2789272.2886818
Uncontrolled keywords: MEG Neuroimaging, Vector-beamforming, Sparse Covariance Estimation, Source Localization and Reconstruction
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: 19 May 2015 16:51 UTC
Last Modified: 17 Jan 2020 16:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48576 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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