[Mne_analysis] Elastic Net Inverse Solutions

Alexandre Gramfort alexandre.gramfort at inria.fr
Tue Sep 19 16:11:59 EDT 2017
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hi,

I was drawn to Elastic Nets because I'm dealing with a dataset that is
> unlikely to be strictly sparse spatially, and I expect a large-ish region
> to have quite highly correlated activity. My understanding and experience
> with L1 regularization is that it chooses a small subset of these
> correlated sources, rather than selecting a set of them (I woudl like the
> solution to capture all of the correlated active sources). L2
> regularization, of course, has the opposite problem, allowing all of the
> sources to be nonzero.
>

this is correct for ENet vs L1 but my reaction is that it then boils down
to the problem of interpretation of an activation foci. ENet will not give
you the extent of the course and it's just a "proxy" towards a region of
confidence / uncertainty around the localized focal dipolar foci the L1
solver will give you.


> In an ideal world I would like an inverse solution that can have a large
> contiguous region of highly correlated active sources, and the rest of the
> brain as zero or close to zero.
>

careful not to interpret this as source extension.


> I should be able to do this with elastic nets by tuning the two
> regularization parameters. Is there a configuration of mixed norm solvers
> that can do it?
>

we never implemented E-Net with MxNE in MNE sorry.

I'll deal with the time dimension later, although I'm open to the idea of
> doing the whole thing in the frequency domain, since I have a long stretch
> of stationary data and the interesting effects are all in one frequency
> band.
>

if it's stationary you can use MxNE after filtering the data in the band of
interest.

HTH
Alex
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