[Mne_analysis] Goodness of fit statistic for TF-MxNE solution?

[Alexandre Gramfort]

hi Per,

usually one run tests between conditions or across subjects.
I am not sure what you want to test. Do you have in mind
chi2 tests as used for dipole fitting?

Maybe have a look at "discrepancy principle" sometimes used
for inverse problems but I am not sure it will solve your problem.

HTH
Alex

PS : I'll have a look at the paper you reference.



On Sun, Sep 14, 2014 at 1:13 AM, Per Arnold Lysne <lysne at unm.edu> wrote:

>  Hello,
>
>
>
> Does anyone know how to judge the goodness of fit of an MEG localization,
> in particular with regard to TF-MxNE? RMSE between the measured sensor data
> and that predicted by the localization seems to be a popular choice, but
> has limited value except in direct comparisons, and no test statistic.
>
>
>
> I am tempted to use Wilk's Lambda, defined as Det(SS_error)/Det(SS_total),
> where SS_error and SS_total are the SSCP matrices as defined in a
> multivariate regression. In this case the data would be the array M of the
> sensor observations (#sensors x #samples), which is modeled by the
> G*Z*Phi-Hermetian term. Rao's-F then provides an approximate p-value.
> Unfortunately neither SS_error nor SS_total are full rank on my data and
> thus the determinants are not available. Additionally, I am struggling with
> the validity of this on a nonstationary system. (Ranks are both ~30, in
> SSCP matrices of 306x306, corresponding to a Neuromag scanner).
>
>
>
> Thanks again,
>
>
>
> Per Lysne, University of New Mexico
>
>
>
> PS: Alex, which regard to your previous concern about using the TF-MxNE
> output with Granger analysis, I am using the nonparametric technique of
> Dhamala, Rangarajan and Ding (2008), which I believe avoids this problem:
> <http://www.sciencedirect.com/science/article/pii/S1053811908001328>
> http://www.sciencedirect.com/science/article/pii/S1053811908001328,
> <http://www.sciencedirect.com/science/article/pii/S1053811908001328>
> http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.018701
>
>    Phys. Rev. Lett. 100, 018701 (2008) - Estimating Granger Causality
> from Fourier and Wavelet Transforms of Time Series Data
>  Experiments in many fields of science and engineering yield data in the
> form of time series. The Fourier and wavelet transform-based nonparametric
> methods are used widely to study the spectral characteristics of these time
> series data. Here, we extend the framework of nonparametric spectral
> methods to include the estimation of Granger causality spectra for
> assessing directional influences. We illustrate the utility of the proposed
> methods using synthetic data from network models consisting of interacting
> dynamical systems.
>  Read more...
> <http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.018701>
>
>
>
>
>
>
>
>
>
> _______________________________________________
> 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/20140914/744e05d0/attachment.html