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
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