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

Using weighted linear spatial decomposition to investigate brain activity through a set of fixed current dipoles
Using weighted linear spatial decomposition to investigate brain activity through a set of fixed current dipoles
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.
current dipole modelling, spherical head model, weighted linear spatial decomposition, remontaging scalp eeg
773-780
James, Christopher J.
c6e71b39-46d2-47c9-a51b-098f428e76e7
Kobayashi, Katsuhiro
a693700c-3a5c-4734-8ecb-3136b98d338a
Gotman, Jean
36be8d79-7b8a-4dcf-abea-c91ab13d3487
James, Christopher J.
c6e71b39-46d2-47c9-a51b-098f428e76e7
Kobayashi, Katsuhiro
a693700c-3a5c-4734-8ecb-3136b98d338a
Gotman, Jean
36be8d79-7b8a-4dcf-abea-c91ab13d3487

James, Christopher J., Kobayashi, Katsuhiro and Gotman, Jean (2000) Using weighted linear spatial decomposition to investigate brain activity through a set of fixed current dipoles. Clinical Neurophysiology, 111 (5), 773-780. (doi:10.1016/S1388-2457(99)00316-8).

Record type: Article

Abstract

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.

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More information

Published date: 2000
Keywords: current dipole modelling, spherical head model, weighted linear spatial decomposition, remontaging scalp eeg

Identifiers

Local EPrints ID: 10798
URI: http://eprints.soton.ac.uk/id/eprint/10798
PURE UUID: bb8baaa7-69e7-436c-97c7-e510368bf287

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Date deposited: 25 Feb 2005
Last modified: 15 Mar 2024 05:00

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Contributors

Author: Christopher J. James
Author: Katsuhiro Kobayashi
Author: Jean Gotman

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