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MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization.

Abstract:

Beamformers are a commonly used method for doing source localization from magnetoencephalography (MEG) data. A key ingredient in a beamformer is the estimation of the data covariance matrix. When the noise levels are high, or when there is only a small amount of data available, the data covariance matrix is estimated poorly and the signal-to-noise ratio (SNR) of the beamformer output degrades. One solution to this is to use regularization whereby the diagonal of the covariance matrix is ampli...

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Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MSD, Psychiatry
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neurosciences
Role:
Author
Journal:
NeuroImage
Volume:
57
Issue:
4
Pages:
1466-1479
Publication date:
2011-08-05
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
URN:
uuid:4cb762b7-48be-4461-a295-7364e7bd0dfc
Source identifiers:
242214
Local pid:
pubs:242214

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