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Optimising beamformer regions of interest analysis
Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forw...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229504/ https://www.ncbi.nlm.nih.gov/pubmed/25134978 http://dx.doi.org/10.1016/j.neuroimage.2014.08.019 |
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author | Oswal, Ashwini Litvak, Vladimir Brown, Peter Woolrich, Mark Barnes, Gareth |
author_facet | Oswal, Ashwini Litvak, Vladimir Brown, Peter Woolrich, Mark Barnes, Gareth |
author_sort | Oswal, Ashwini |
collection | PubMed |
description | Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forward model. Increasing the number of M/EEG sensors, increases the quantity of data required for accurate covariance matrix estimation. Often however we have a prior hypothesis about the site of, or the signal of interest. Here we show how this prior specification, in combination with optimal estimations of data dimensionality, can give enhanced beamformer performance for relatively short data segments. Specifically we show how temporal (Bayesian Principal Component Analysis) and spatial (lead field projection) methods can be combined to produce improvements in source estimation over and above employing the approaches individually. |
format | Online Article Text |
id | pubmed-4229504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-42295042014-11-15 Optimising beamformer regions of interest analysis Oswal, Ashwini Litvak, Vladimir Brown, Peter Woolrich, Mark Barnes, Gareth Neuroimage Technical Note Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forward model. Increasing the number of M/EEG sensors, increases the quantity of data required for accurate covariance matrix estimation. Often however we have a prior hypothesis about the site of, or the signal of interest. Here we show how this prior specification, in combination with optimal estimations of data dimensionality, can give enhanced beamformer performance for relatively short data segments. Specifically we show how temporal (Bayesian Principal Component Analysis) and spatial (lead field projection) methods can be combined to produce improvements in source estimation over and above employing the approaches individually. Academic Press 2014-11-15 /pmc/articles/PMC4229504/ /pubmed/25134978 http://dx.doi.org/10.1016/j.neuroimage.2014.08.019 Text en © 2014 The Authors https://creativecommons.org/licenses/by/3.0/This work is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/) . |
spellingShingle | Technical Note Oswal, Ashwini Litvak, Vladimir Brown, Peter Woolrich, Mark Barnes, Gareth Optimising beamformer regions of interest analysis |
title | Optimising beamformer regions of interest analysis |
title_full | Optimising beamformer regions of interest analysis |
title_fullStr | Optimising beamformer regions of interest analysis |
title_full_unstemmed | Optimising beamformer regions of interest analysis |
title_short | Optimising beamformer regions of interest analysis |
title_sort | optimising beamformer regions of interest analysis |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229504/ https://www.ncbi.nlm.nih.gov/pubmed/25134978 http://dx.doi.org/10.1016/j.neuroimage.2014.08.019 |
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