Integrating MEG, EEG and fMRI data

How can we integrate MEG, EEG, and fMRI ?
d. Use of distributed sources and regularization of
Linear inverse Estimation (LE)
• since the number of dipole components (5,000-10,000) is
much larger than the number of measurements (150-500),
we must minimize the following expression (cost
function):
⎜⎜Ax - b⎜⎜ + λ 2 ⎜⎜Cx ⎜⎜
where λ is the so-called regularization parameter and C is
simply a weight matrix that depends on the head model.
• By appropriately shaping C we can take into account the
possible constraints used in various LE analysis methods
(Minimum Norm, SLORETA, etc.), but also the fMRI
constraints