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A non-Gaussian LCMV beamformer for MEG source reconstruction

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Hamid R. Mohseni, Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, United Kingdom
  • Morten L. Kringelbach
  • Mark W. Woolrich, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, United Kingdom
  • Tipu Z. Aziz, Oxford Functional Neurosurgery, Nuffield Department of Surgery, John Radcliffe Hospital, United Kingdom
  • Penny Probert Smith, Institute of Biomedical Engineering, School of Engineering Science, University of Oxford, Unknown

Evidence suggests that magnetoencephalogram (MEG) data have characteristics with non-Gaussian distribution, however, standard methods for source localisation assume Gaussian behaviour. We present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity in the MEG data. By providing a Bayesian formulation for linearly constraint minimum variance (LCMV) beamformer, we extend this approach and show that how the source probability density function (pdf), which is not necessarily Gaussian, can be estimated. The proposed non-Gaussian beamformer is shown to give better spatial estimates than the LCMV beamformer, in both simulations incorporating non-Gaussian signal and in real MEG measurements.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Number of pages5
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication year18 Oct 2013
Pages1247-1251
Article number6637850
ISBN (print)9781479903566
DOIs
Publication statusPublished - 18 Oct 2013

    Research areas

  • LCMV-beamformer, Magnetoencephalography, non-Gaussian, source reconstruction

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