[FieldTrip] Set regularization parameter after SSS

Tue Aug 21 15:38:23 CEST 2018

Dear Matti et al.,

Matti, in relating to your original point, where you state something about the beamformer algorithms (lcmv/possibly dics) in FT: in addition to Robert and Vladimir’s comments, and you might already have seen this, the somewhat obscure optional input argument to beamformer_lcmv/beamformer_dics, called ‘subspace’ (containing an MxNsensor projection matrix), can be used to generically project the data (as well as the forward operator) to a subspace (e.g.: prewhitened subspace, truncated SVD, diagonal weighting matrix as per the sensor type). It’s not documented well, nor is it applied automatically. But if you want, you can do it :).

Best ,
JM

> On 21 Aug 2018, at 15:16, 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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