TY - JOUR
T1 - Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique
AU - Sekihara, K.
AU - Nagarajan, S. S.
AU - Poeppel, D.
AU - Marantz, A.
AU - Miyashita, Y.
N1 - Funding Information:
Manuscript received March 17, 2000; revised April 6, 2001. The work of S. S. Nagarajan was supported by a grant from the Whitaker Foundation. This work was carried out as part of the MIT-JST International Cooperative Research Project “Mind Articulation.” Asterisk indicates corresponding author. *K. Sekihara is with the Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Asahigaoka 6-6, Hino, Tokyo 191-0065, Japan (e-mail: [email protected]). S. S. Nagarajan is with the Department of Bioengineering, University of Utah, Salt Lake City, UT 84112-9202 USA. D. Poeppel is with the Department of Linguistics and Biology, University of Maryland, College Park, MD 20742 USA. A. Marantz is with the Department of Linguistics and Philosophy, Massachusetts Institute of Technology, Cambridge, MA 02139 USA. Y. Miyashita is with the Department of Physiology, The University of Tokyo, School of Medicine, Hongo, Tokyo 113-0033, Japan. Publisher Item Identifier S 0018-9294(01)05134-5.
PY - 2001
Y1 - 2001
N2 - We have developed a method suitable for reconstructing spatio-temporal activities of neural sources by using magnetoencephalogram (MEG) data. The method extends the adaptive beamformer technique originally proposed by Borgiotti and Kaplan to incorporate the vector beamformer formulation in which a set of three weight vectors are used to detect the source activity in three orthogonal directions. The weight vectors of the vector-extended version of the Borgiotti-Kaplan beamformer are then projected onto the signal subspace of the measurement covariance matrix to obtain the final form of the proposed beamformer's weight vectors. Our numerical experiments show that both spatial resolution and output signal-to-noise ratio of the proposed beamformer are significantly higher than those of the minimum-variance-based vector beamformer used in previous investigations. We also applied the proposed beamformer to two sets of auditory-evoked MEG data, and the results clearly demonstrated the method's capability of reconstructing spatio-temporal activities of neural sources.
AB - We have developed a method suitable for reconstructing spatio-temporal activities of neural sources by using magnetoencephalogram (MEG) data. The method extends the adaptive beamformer technique originally proposed by Borgiotti and Kaplan to incorporate the vector beamformer formulation in which a set of three weight vectors are used to detect the source activity in three orthogonal directions. The weight vectors of the vector-extended version of the Borgiotti-Kaplan beamformer are then projected onto the signal subspace of the measurement covariance matrix to obtain the final form of the proposed beamformer's weight vectors. Our numerical experiments show that both spatial resolution and output signal-to-noise ratio of the proposed beamformer are significantly higher than those of the minimum-variance-based vector beamformer used in previous investigations. We also applied the proposed beamformer to two sets of auditory-evoked MEG data, and the results clearly demonstrated the method's capability of reconstructing spatio-temporal activities of neural sources.
KW - Beamformer
KW - Biomagnetism
KW - Functional neuroimaging
KW - MEG inverse problems
KW - Magnetoencephalography
KW - Neuromagnetic signal processing
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U2 - 10.1109/10.930901
DO - 10.1109/10.930901
M3 - Article
C2 - 11442288
AN - SCOPUS:0034972842
SN - 0018-9294
VL - 48
SP - 760
EP - 771
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 7
ER -