Time-frequency MEG-MUSIC algorithm

Kensuke Sekihara, Srikantan Nagarajan, David Poeppel, Yasushi Miyashita

Research output: Contribution to journalArticlepeer-review


We propose a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. The method is based on the multiple-signalclassiflcation (MUSIC) algorithm and it calculates a time-frequency matrix in which diagonal and off-diagonal terms are the auto and crosstime-frequency distributions of multichannel MEG recordings, respectively. The method averages this time-frequency matrix over the time-frequency region of interest. The locations of neural sources are then estimated by checking the orthogonality between the noise subspace of this averaged matrix and the sensor lead field. Accordingly, the method allows us to estimate the locations of neural sources from each time-frequency component. A computer simulation was performed to test the validity of the proposed method, and the results demonstrate its effectiveness.

Original languageEnglish (US)
Pages (from-to)92-97
Number of pages6
JournalIEEE Transactions on Medical Imaging
Issue number1
StatePublished - 1999


  • Biomagnetism, biomédical signal processing, inverse problems, time-frequency analysis

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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