[Mne_analysis] Single trial output from TF-MxNE via MVAR regression?

[Per Arnold Lysne]

Hi Alex,

    I have uploaded the function I am using to accomplish this to https://github.com/perlysne/pal_repository along with figures showing output for magnetometers (showing success) and gradiometers (no success). This is based on median nerve stimulation data collected on a Neuromag system. The localizations are very good.

    I'm not sure if you would like a complete, running script, or that it be applied to MNE sample data. I can provide either one if needed.

    Thanks again,

-Per
________________________________________
From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Alexandre Gramfort <alexandre.gramfort at telecom-paristech.fr>
Sent: Monday, October 13, 2014 1:49 PM
To: Discussion and support forum for the users of MNE Software
Subject: Re: [Mne_analysis] Single trial output from TF-MxNE via MVAR   regression?

hi Per,

if you want me to have a look at your code please share a minimal code
snippet using MNE sample data on https://gist.github.com

Alex


On Mon, Oct 13, 2014 at 9:09 PM, Per Arnold Lysne <lysne at unm.edu> wrote:
> Hi Alex,
>
>     With regard to creating an inverse operator which can be used to produce individual trial output from the tf-mxne solution: I am attempting this by regressing the tf_mixed_norm timecourses onto the evoked average sensor data. This appears to work for magnetometer data but not gradiometers. I expect this is something simple, but I haven't been able to figure it out. I am using the evoked data without whitening (but which doesn't seem to make a difference) or any other preprocessing except for 2-100Hz filtering on the continuous data, and trial rejection based on thresholding the EOG channel. Likewise I am using the 'X' output from tf_mixed_norm without any alterations.
>
>     I would like to make this work for both Neuromag 306 and CTF 275 systems. Do you have any ideas? Thanks again,
>
> Per Lysne
> University of New Mexico
> ________________________________________
> From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Alexandre Gramfort <alexandre.gramfort at telecom-paristech.fr>
> Sent: Tuesday, September 9, 2014 7:56 AM
> To: Discussion and support forum for the users of MNE Software
> Subject: Re: [Mne_analysis] Single trial output from TF-MxNE via MVAR   regression?
>
> hi Per,
>
> what you describe is possible ie run a least square inverse on time-frequencies
> atoms selected by TF-MxNE. This is however not implemented.
>
> In terms of approach what bothers me is the use of a stationary model
> such as Granger on time courses which are obtained to carefully model
> the evoked data which are transient / non-stationary effects.
>
> Alex
>
>
> On Tue, Sep 9, 2014 at 1:51 AM, Per Arnold Lysne <lysne at unm.edu> wrote:
>> Hello All,
>>
>>
>>
>>     My apologies for bringing up the same question regarding single trial
>> output from tf-mxne several times - I hope my questions are getting better
>> each time.
>>
>>
>>
>>     As Dr. Gramfort has explained in the past, tf-mxne solves for both
>> locations and their timecourses concurrently, and therefore there is no
>> separate inverse operator which can be returned from this process and
>> reused. This solution is based on an average evoked response, so no
>> trial-wise output is possible. My problem is that I would like to use the
>> tf-mxne output to estimate spectral Granger causality, and the spectral
>> estimates this depends upon requires single trial output from the neural
>> sources. It would be perfectly acceptable to use an average evoked response
>> for the tf-mxne localizations, if it was then possible to apply this
>> solution to obtain single trial timecourses at the resulting sources.
>>
>>
>>
>>     As a solution to this I propose to MVAR regress the tf-mxne timecourses,
>> representing the brain-space response, onto the average evoked response as
>> seen at the sensors. The relationship between the sources and the sensors
>> should be linear and constant in time, and this should yield a set of MVAR
>> regression coefficients which may then be used as the inverse operator to
>> generate single trial brain-space timecourses.
>>
>>
>>
>>     I believe that the solution timecourses may be altered somewhat by doing
>> this, as tf-mxne is based on discarding Gabor atoms as dictated by the mixed
>> norm cost function, and that there is no guarantee that the resulting
>> timecourses are directly linearly related to the sensor measurements, but I
>> hope this effect to be minimal. (In my application, which depends upon
>> maintaining linearity throughout the analysis pipeline, this may even be
>> beneficial.)
>>
>>
>>
>>     Does this seem appropriate?
>>
>>
>>
>> Thanks again,
>>
>>
>>
>> Per Lysne, The University of New Mexico
>>
>>
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