MEG/EEG source modeling for localization and imaging of brain activity
For clarity purposes, we will not attempt to formalize in a general, overly technical way, the classes of approaches to MEG/EEG source estimation. We will rather adopt a pragmatic standpoint, observing that two main chapels have developed quite separately: the localization and the imaging approaches respectively (Salmelin & Baillet, 2009). Our purpose here is to mark methodological landmarks and stress on differences, similarities, and their respective assets.
Two approaches for two different perspectives on imaging brain activity.
Computational approaches to source localization attempt to mimic the talent of electrophysiologists, with a more quantitative benefit though.
The inherent difficulties to source localization with multiple generators and noisy data have led signal processors to develop alternative approaches, most notably in the glorious field of radar and sonar in the 1970’s.
MEG/EEG source images represent estimations of the global neural current intensity maps, distributed within the entire brain volume or constrained at the cortical surface.
Modeling implies dealing with uncertainty. MEG/EEG source estimation has uncertainty everywhere. It is therefore reasonable to question how sensitive are the models to possible sources of errors and bias. This concerns the appraisal of the source model, which general methodology has been adapted to MEG/EEG rather recently and is now achieving significant maturity.