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

[Per Arnold Lysne]

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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