Non-gaussian probabilistic MEG source localisation based on kernel density estimation
There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribu...
| Main Authors: | , , , , , |
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| Format: | Journal article |
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2014
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| _version_ | 1797088304144842752 |
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| author | Mohseni, H Baker, A Probert-Smith, P Kringelbach, M Woolrich, M Aziz, T |
| author_facet | Mohseni, H Baker, A Probert-Smith, P Kringelbach, M Woolrich, M Aziz, T |
| author_sort | Mohseni, H |
| collection | OXFORD |
| description | There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate. © 2013 The Authors. |
| first_indexed | 2024-03-07T02:48:05Z |
| format | Journal article |
| id | oxford-uuid:acb41eca-3d7f-4061-8243-ca098196aec0 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:48:05Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:acb41eca-3d7f-4061-8243-ca098196aec02022-03-27T03:30:49ZNon-gaussian probabilistic MEG source localisation based on kernel density estimationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:acb41eca-3d7f-4061-8243-ca098196aec0Symplectic Elements at Oxford2014Mohseni, HBaker, AProbert-Smith, PKringelbach, MWoolrich, MAziz, TThere is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate. © 2013 The Authors. |
| spellingShingle | Mohseni, H Baker, A Probert-Smith, P Kringelbach, M Woolrich, M Aziz, T Non-gaussian probabilistic MEG source localisation based on kernel density estimation |
| title | Non-gaussian probabilistic MEG source localisation based on kernel density estimation |
| title_full | Non-gaussian probabilistic MEG source localisation based on kernel density estimation |
| title_fullStr | Non-gaussian probabilistic MEG source localisation based on kernel density estimation |
| title_full_unstemmed | Non-gaussian probabilistic MEG source localisation based on kernel density estimation |
| title_short | Non-gaussian probabilistic MEG source localisation based on kernel density estimation |
| title_sort | non gaussian probabilistic meg source localisation based on kernel density estimation |
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