[Mne_analysis] Elastic Net Inverse Solutions

[Emily Stephen]

Thanks, Alex! I'll keep thinking carefully about my options.

Emily

On Tue, Sep 19, 2017 at 4:11 PM, Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

> 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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