Noise covariance incorporated MEG-MUSIC algorithm: A method for multiple-dipole estimation tolerant of the influence of background brain activity

Kensuke Sekihara, David Poeppel, Alec Marantz, Hideaki Koizumi, Yasushi Miyashita

Research output: Contribution to journalArticle


This paper proposes a method of localizing multiple current dipoles from spatio-temporal biomagnetic data. The method is based on the multiple signal classification (MUSIC) algorithm and is tolerant of the influence of background brain activity. In this method, the noise covariance matrix is estimated using a portion of the data that contains noise, but does not contain any signal information. Then, a modified noise subspace projector is formed using the generalized eigenvectors of the noise and measured-data covariance matrices. The MUSIC localizer is calculated using this noise subspace projector and the noise covariance matrix. The results from a computer simulation have verified the effectiveness of the method. The method was then applied to source estimation for auditory-evoked fields elicited by syllable speech sounds. The results strongly suggest the method's effectiveness in removing the influence of background activity.

Original languageEnglish (US)
Pages (from-to)839-847
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number9
StatePublished - Sep 1 1997



  • Array signal processing
  • Biomagnetics
  • Biomedical electromagnetic imaging
  • Biomedical signal processing
  • Fuctional brain imaging
  • Inverse problems

ASJC Scopus subject areas

  • Biomedical Engineering

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