Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique

K. Sekihara, S. S. Nagarajan, D. Poeppel, A. Marantz, Y. Miyashita

Research output: Contribution to journalArticle

Abstract

We have developed a method suitable for reconstructing spatio-temporal activities of neural sources by using magnetoencephalogram (MEG) data. The method extends the adaptive beamformer technique originally proposed by Borgiotti and Kaplan to incorporate the vector beamformer formulation in which a set of three weight vectors are used to detect the source activity in three orthogonal directions. The weight vectors of the vector-extended version of the Borgiotti-Kaplan beamformer are then projected onto the signal subspace of the measurement covariance matrix to obtain the final form of the proposed beamformer's weight vectors. Our numerical experiments show that both spatial resolution and output signal-to-noise ratio of the proposed beamformer are significantly higher than those of the minimum-variance-based vector beamformer used in previous investigations. We also applied the proposed beamformer to two sets of auditory-evoked MEG data, and the results clearly demonstrated the method's capability of reconstructing spatio-temporal activities of neural sources.

Original languageEnglish (US)
Pages (from-to)760-771
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume48
Issue number7
DOIs
StatePublished - 2001

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Covariance matrix
Signal to noise ratio
Experiments

Keywords

  • Beamformer
  • Biomagnetism
  • Functional neuroimaging
  • Magnetoencephalography
  • MEG inverse problems
  • Neuromagnetic signal processing

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique. / Sekihara, K.; Nagarajan, S. S.; Poeppel, D.; Marantz, A.; Miyashita, Y.

In: IEEE Transactions on Biomedical Engineering, Vol. 48, No. 7, 2001, p. 760-771.

Research output: Contribution to journalArticle

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