[FieldTrip] Set regularization parameter after SSS

[Vladimir Litvak]

I usually reduce the rank to 50 just to be safe. Although in theory,
the rank should be 80 with the default settings (if I'm not mistaken),
in practice the data are not full rank and there are will be many SVD
components with small eigenvalues. Maybe 50 is an overkill but seems
to work OK in practice.

Vladimir
On Tue, Aug 21, 2018 at 2:35 PM Matti Stenroos <matti.stenroos at aalto.fi> wrote:
>
> Hi Vladimir, Robert,
>
> Thanks for your input!
>
> I've been playing with linear estimates & SSS-processed data. Based on
> my experience, I agree with Vladimir that dropping the rank to 60--70
> (depending on the number of max filter coeffs etc) is the way to go. If
> whitening (via noise covariance matrix) is used, some regularization to
> the whitener is probably appropriate.
>
> I don't have strong opinion regarding "only gradiometers" or
> "gradio+magneto". I'd say there is no general answer; rather it depends
> on what kind on inverse method you apply = how you use your data there.
> In resolution analysis of minimum-norm estimates, the difference is small.
>
> Data fusing = sensor scaling should not be that difficult; pre-weighting
> the data with 1/sqrt(mvars), where mvars has mean noise/data variances
> per sensotype. But, as the Max reconstructs all data using the same
> multipole series with ~64 components, rank will not really increase ---
> if there has been 64 max filter components, useful ranks of
> magnetometers, gradiometers, and the whole MEG are of the order of 60--64.
>
> Cheers,
>    Matti
>
>
> On 2018-08-21 15:02, Vladimir Litvak wrote:
> > Hi Luca and Matti,
> >
> > We have recently looked into beamforming on Elekta with several
> > methods people including Robert and Alex Gramfort. One thing we
> > discovered is that beamforming can completely fail when small
> > regularisation (like 1% or 5% which most people use) is applied to
> > data after SSS. The theoretical underpinning for this has not been
> > figured out yet, but in practice, the more robust thing to do is to
> > reduce the data dimensionality and set lambda to zero. I attach an
> > example developed for the phantom data we got from Aston MEG that
> > shows how this can be done in FT. If you want, I can share the phantom
> > example with you so that you can verify that this approach works.
> >
> > Regarding sensor type fusion, I would suggest that you only limit your
> > analysis to one sensor type. Most Neuromag people use the planar
> > gradiometers. The reason for this suggestion is that after SSS,
> > channels of both types are linear combinations of the same basis
> > vectors and contain redundant information so trying to fuse them is
> > more a headache than anything else. There is a paper saying this in
> > more detail http://www.mdpi.com/1424-8220/17/12/2926/html
> >
> > Best,
> >
> > Vladimir
> >
> > On Mon, Aug 20, 2018 at 2:35 PM Luca Kaiser <luca.kaiser at web.de> wrote:
> >>
> >> Hi Matti,
> >>
> >> sure-sorry and thanks for your quick reply. So here is what I am doing using the average covariance matrix (so data covariance).
> >>
> >> cfg=[];
> >> cfg.covariance='yes';
> >> cfg.channel=data.label;
> >> avg_data=ft_timelockanalysis(cfg, data);
> >>
> >> cfg=[];
> >> cfg.method='lcmv';
> >> cfg.grid=lf;
> >> cfg.vol=hdm;
> >> cfg.lcmv.keepfilter='yes';
> >> cfg.lcmv.fixedori= 'yes';
> >> cfg.lcmv.lambda= '10%'; %0% rank deficient data-use stronger regularization??
> >> lcmv_avg=ft_sourceanalysis(cfg, avg_data);
> >>
> >>
> >> Best,
> >>
> >> Luca
> >>
> >>
> >> Gesendet: Montag, 20. August 2018 um 15:10 Uhr
> >> Von: "Matti Stenroos" <matti.stenroos at aalto.fi>
> >> An: "Luca Kaiser" <luca.kaiser at web.de>
> >> Betreff: Re: [FieldTrip] Set regularization parameter after SSS
> >> Dear Luca,
> >>
> >> I think you'd need to tell a bit more, as the meaning of "lambda"
> >> depends on the algorithm you are using. Also "the covariance matrix" is
> >> not uniquely defined --- are you talking about noise covariance or
> >> measurement covariance?
> >>
> >> In general, the eigenvalue idea you had read does not make sense --- the
> >> eigenvalue text deals with condition numbers, while lambda does, in
> >> general, not relate directly to that. The advice might have been that
> >> "set lambda so that the ratio of largest and smallest eigenvalues is
> >> 1000"...
> >>
> >> Cheers,
> >> Matti
> >> also working with low-rankingMax-filtered MEG and coincidentally
> >> having a lot of covariance metrics on screen at the moment
> >>
> >>
> >>
> >> On 2018-08-20 15:39, Luca Kaiser wrote:
> >>> Dear FieldTrip community,
> >>> I am using ft_sourceanalysis on SSS preprocessed (neuromag) data. I
> >>> wonder if there are any suggestions on how to set lambda in this case?
> >>> My covariance matrix is rank deficient (rank 60, 306 sensors). I read in
> >>> the mailing list that I would need to look at the eigenvalues of XX' and
> >>> set lambda to be 1/1000 of the largest eigenvalue. However, I do not
> >>> really understand why to divide by 1/1000?
> >>> Every help would be very much appreciated,
> >>> Thanks!
> >>> Luca
> >>>
> >>>
> >>>
> >>>
> >>>
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