Skip to main content
Kent Academic Repository

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) (KAR id:48576)

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

magnetoencephalography (MEG) data in the literature. However, the existing theoretical analyses

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

is also assumed known. The information about the effects of the MEG spatial and temporal dimensions on the consistency of vector-beamforming is incomplete.

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.

A general asymptotic theory is developed for

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

when the signal-to-noise ratio is low.

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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jian Zhang
Date Deposited: 19 May 2015 16:51 UTC
Last Modified: 09 Dec 2022 05:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48576 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.