Reconstructing spatio-temporal activities of neural sources from magnetoencephalographic data using a vector beamformer

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

Research output: Contribution to journalConference articlepeer-review

Abstract

We have developed a method suitable for reconstructing spatio-temporal activities of neural sources using MEG data. Our method is based on an adaptive beam-former technique. It extends a beamformer originally proposed by Borgiotti and Kaplan to a vector beamformer formulation in which three sets of weight vectors are used to detect the source activity in three orthogonal directions. The weight vectors of this vector-extension of the Borgiotti-Kaplan beamformer are then projected onto the signal subspace of the measurement covariance matrix to obtain a final form of the proposed beamformer's weight vectors. Our numerical experiments demonstrated the effectiveness of the proposed beamformer.

Original languageEnglish (US)
Pages (from-to)2021-2024
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - 2001
Event2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing - Salt Lake, UT, United States
Duration: May 7 2001May 11 2001

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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