• DocumentCode
    1252374
  • Title

    MEG covariance difference analysis: a method to extract target source activities by using task and control measurements

  • Author

    Sekihara, Kensuke ; Poeppel, David ; Marantz, Alec ; Phillips, Colin ; Koizumi, Hideaki ; Miyashita, Yasushi

  • Author_Institution
    Mind Articulation Project, Japan Sci. & Technol. Corp., Tokyo, Japan
  • Volume
    45
  • Issue
    1
  • fYear
    1998
  • Firstpage
    87
  • Lastpage
    97
  • Abstract
    A method is proposed for extracting target dipole-source activities from two sets of evoked magnetoencephalographic (MEG) data, one measured using task stimuli and the other using control stimuli. The difference matrix between the two covariance matrices obtained from these two measurements is calculated, and a procedure similar to the MEG-multiple signal classification (MUSIC) algorithm is applied to this difference matrix to extract the target dipole-source configuration. This configuration corresponds to the source-configuration difference between the two measurements. Computer simulation verified the validity of the proposed method. The method was applied to actual evoked-field data obtained from simulated task-and-control experiments. In these measurements, a combination of auditory and somatosensory stimuli was used as the task stimulus and the somatosensory stimulus alone was used as the control stimulus. The proposed covariance difference analysis successfully extracted the target auditory source and eliminated the disturbance from the somatosensory sources.
  • Keywords
    covariance matrices; hearing; magnetoencephalography; medical signal processing; somatosensory phenomena; MEG-multiple signal classification algorithm; auditory source; computer simulation; control stimuli; covariance difference analysis; difference matrix; evoked magnetoencephalographic data sets; somatosensory stimulus; source-configuration difference; target dipole-source activities extraction method; task stimuli; Biomedical imaging; Biomedical measurements; Biomedical signal processing; Brain; Classification algorithms; Covariance matrix; Data mining; Electromagnetic measurements; Multiple signal classification; Pattern classification; Algorithms; Computer Simulation; Evoked Potentials, Auditory; Evoked Potentials, Somatosensory; Humans; Image Enhancement; Magnetoencephalography; Male; Models, Neurological; Reference Values; Signal Processing, Computer-Assisted;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/10.650357
  • Filename
    650357