TY - JOUR
T1 - Time-frequency MEG-MUSIC algorithm
AU - Sekihara, Kensuke
AU - Nagarajan, Srikantan
AU - Poeppel, David
AU - Miyashita, Yasushi
N1 - Funding Information:
Manuscript received July 15, 1998; revised December 7, 1998. This work was supported in part by the Centre National de la Recherche Scientifique. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was M. Viergever. Asterisk indicates corresponding author. *K. Sekihara is with the Mind Articulation Project, Japan Science and Technology Corporation (JST), Yushima 4-9-2, Bunkyo, Tokyo 113-0034 Japan (e-mail: [email protected]).
PY - 1999
Y1 - 1999
N2 - 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.
AB - 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.
KW - Biomagnetism, biomédical signal processing, inverse problems, time-frequency analysis
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U2 - 10.1109/42.750262
DO - 10.1109/42.750262
M3 - Article
C2 - 10193700
AN - SCOPUS:0032587694
SN - 0278-0062
VL - 18
SP - 92
EP - 97
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 1
ER -