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 b ased 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 int erest. The algorithm is based on weighted linear spatial decomposition in o rder to obtain a weighted solution to the inverse problem. An EEG data matr ix is first weighted in favour of a single dipole in the set. The dipole mo ment 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 lea st-squares solution and our weighted version. The average cross-correlation between channels was calculated for each case. The first method resulted i n a mean drop in cross-correlation of 16.5% from that of the scalp. Our met hod resulted in a reduction of 34.5%. Conclusions: Our method gives a more spatially decorrelated signal in regio ns of interest (although it is not intended as an accurate localization too l). Subsequent analysis is more robust and less likely to be dependent on s pecific recording montages. This is more than could be obtained using a sta ndard least-squares solution using the same model. (C) 2000 Elsevier Scienc e Ireland Ltd. All rights reserved.