[Mne_analysis] Elastic Net Inverse Solutions

[Alexandre Gramfort]

Hi Emily


Has anyone done an inverse solution using Elastic Nets?
>

you do have sparse solvers with L1 penalties in MNE. We call them MxNE or
TF-MxNE and are used with the (tf_)mixed_norm functions.

See:

https://martinos.org/mne/stable/auto_examples/inverse/
plot_mixed_norm_inverse.html

https://martinos.org/mne/stable/auto_examples/inverse/
plot_time_frequency_mixed_norm_inverse.html

such solvers take into account the spatio-temporal structure of the M/EEG
data.

See the referenced papers.

If you want to use with own solver and eventually use the sklearn sparse
solvers (that will only work with 1 time instant) you can start with this
example:

http://martinos.org/mne/dev/auto_examples/inverse/plot_
custom_inverse_solver.html

Let me know if you have any questions.

I've been working with these for some years now.

HTH
Alex


> Any pointers for setting it up using as much pre-existing, well-tested
> code as possible? E.g. I see that sklearn has Elastic Net functionality --
> any best practices for using sklearn linear models with the mne-python data
> structures?
>
> Thanks,
> Emily
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20170919/7c9a0c92/attachment.html