MEG (Magnetoencephalography) Program

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Appraisal of MEG/EEG source models


Throughout these pages, we have been dealing with modeling, and modeling implies dealing with uncertainty. MEG/EEG source estimation has uncertainty everywhere: data are complex and contaminated with various nuisances, source models are simplistic, head models have approximated geometries and conductivity properties, the choice of priors has its share of subjectivity, etc.


It is therefore reasonable to question how sensitive the numerical methods at stake are to these possible sources of errors and bias. This concerns the appraisal of the source model, which general methodology has been adapted to MEG/EEG just recently and is now achieving significant maturity.
 

(1) Confidence intervals

It is important to evaluate the confidence one might acknowledge to a given model. In other words, we are now looking for error bars that would define a confidence interval about the estimated values of a source model.

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(2) Statistical inference

Questions like: ‘How different is the dipole location between these two experimental conditions? ’ and ‘Are source amplitudes larger in such condition that in a control condition? ’ belong to statistical inference from experimental data.

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