EEG source localization: implementing the spatio-temporal decomposition approach

Abstract

Objectives: The spatio-temporal decomposition (STD) approach was used to localize the sources of simulated electroencephalograms (EEGs) to gain experience with the approach for analyzing real data.

Methods: The STD approach used is similar to the multiple signal classification method (MUSIC) in that it requires the signal subspace containing the sources of interest to be isolated in the EEG measurement space. It is different from MUSIC in that it allows more general methods of spatio-temporal decomposition to be used that may be better suited to the background EEG.

Results: If the EEG data matrix is not corrupted by noise, the STD approach can be used to locate multiple dipole sources of the EEG one at a time without a priori knowledge of the number of active sources in the signal space. In addition, the common-spatial-patterns method of spatio-temporal decomposition is superior to the eigenvector decomposition for localizing activity that is ictal in nature.

Conclusions: The STD approach appears to be able to provide a means of localizing the equivalent dipole sources of realistic brain sources and that, even under difficult noise conditions and only 2 or 3 s of available EEG, the precision of the localization can be as low as a few mm.