TY - JOUR

T1 - Using weighted linear spatial decomposition to investigate brain activity through a set of fixed current dipoles

AU - James, Christopher J.

AU - Kobayashi, Katsuhiro

AU - Gotman, Jean

N1 - Funding Information:
This work was made possible by funding from the Canadian Medical Research Council grant number 10-189 and in part by UK EPSRC grant number GR/L94673. We gratefully acknowledge the technical assistance of EEG technologists Nicole Drouin and Lorraine Allard in obtaining the telemetered EEG data from the Montreal Neurological Hospital. We also thank the reviewers of this article for their valuable suggestions.

PY - 2000/5/1

Y1 - 2000/5/1

N2 - Objectives: We developed a method with the aim of decorrelating scalp EEG based on a set of spatial constraints. Methods: We assume that the scalp EEG can be modelled by a small number of current dipoles of fixed location and orientation, placed at regions of interest. The algorithm is based on weighted linear spatial decomposition in order to obtain a weighted solution to the inverse problem. An EEG data matrix is first weighted in favour of a single dipole in the set. The dipole moment is then calculated from the weighted EEG by the pseudo-inverse method. This is repeated for each dipole. Results: Six seizures were processed from 4 patients using the standard least-squares solution and our weighted version. The average cross- correlation between channels was calculated for each case. The first method resulted in a mean drop in cross-correlation of 16.5% from that of the scalp. Our method resulted in a reduction of 34.5%. Conclusions: Our method gives a more spatially decorrelated signal in regions of interest (although it is not intended as an accurate localization tool). Subsequent analysis is more robust and less likely to be dependent on specific recording montages. This is more than could be obtained using a standard least-squares solution using the same model. (C) 2000 Elsevier Science Ireland Ltd.

AB - Objectives: We developed a method with the aim of decorrelating scalp EEG based on a set of spatial constraints. Methods: We assume that the scalp EEG can be modelled by a small number of current dipoles of fixed location and orientation, placed at regions of interest. The algorithm is based on weighted linear spatial decomposition in order to obtain a weighted solution to the inverse problem. An EEG data matrix is first weighted in favour of a single dipole in the set. The dipole moment is then calculated from the weighted EEG by the pseudo-inverse method. This is repeated for each dipole. Results: Six seizures were processed from 4 patients using the standard least-squares solution and our weighted version. The average cross- correlation between channels was calculated for each case. The first method resulted in a mean drop in cross-correlation of 16.5% from that of the scalp. Our method resulted in a reduction of 34.5%. Conclusions: Our method gives a more spatially decorrelated signal in regions of interest (although it is not intended as an accurate localization tool). Subsequent analysis is more robust and less likely to be dependent on specific recording montages. This is more than could be obtained using a standard least-squares solution using the same model. (C) 2000 Elsevier Science Ireland Ltd.

KW - Current dipole modelling

KW - Remontaging scalp EEG

KW - Spherical head model

KW - Weighted linear spatial decomposition

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U2 - 10.1016/S1388-2457(99)00316-8

DO - 10.1016/S1388-2457(99)00316-8

M3 - Article

C2 - 10802446

AN - SCOPUS:0034194399

SN - 1388-2457

VL - 111

SP - 773

EP - 780

JO - Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control

JF - Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control

IS - 5

ER -