Methods for separating temporally overlapping sources of neuroelectric data

Abstract

The localization of intracranial sources of EEG or MEG signals can be misled by the combined effect of several sources, as illustrated by simulated MEG data in which two of the three dipolar sources have slightly out of phase activity and partly complementary scalp topographies. These data were analysed by three different source localization methods. Fitting a single source to each sequential topography worked perfectly when only one source was active; this could also account for as much as 95% of the spatial variance of topographies resulting from two overlapping sources, although the solution was then far from any source. A principal component analysis approach followed by an oblique rotation (fitting one source to the spatial aspect of each component) correctly localized two of the sources but severely mislocated the source that was never active alone. Spatiotemporal source modeling (simultaneously fitting a set of sources to all consecutive topographies) correctly localized all three sources, provided that the parameter optimization method could escape sub-optimal local minima of the error function. Temporally overlapping sources can thus be separated and correctly identified if the mathematical model is adequate and the optimization procedure is well adapted.