[Mne_analysis] tf_mixed_norm and PCA option?

[Per Arnold Lysne]

Hi Alex,

    If I understand you correctly, the PCA is being used in a data reduction capacity across the sensors, or the spatial dimension. In which case the sensor values at each sample represent the variables (204 in your example), and the samples in time represent the observations. The outcome of this are the component scores (80) at each sample which become the new timecourses.

    The motivation for tf-mxne is do avoid assuming temporal stationarity of the neural sources. By performing the PCA solution over time, i.e. assuming that all of the observations come from the same population, do we not make the same error? This seems very similar to the traditional method of estimating the parameters of an MVAR system over time?

    Thanks for your patience,

-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: Tuesday, September 30, 2014 2:33 PM
To: Discussion and support forum for the users of MNE Software
Subject: Re: [Mne_analysis] tf_mixed_norm and PCA option?

hi Per,

>     Regarding the Boolean 'pca' option to tf_mixed_norm.py, I'm having
> trouble figuring what this is doing. In either the T or F case, I get a
> message saying "Not doing PCA for MEG", but then in the T case another
> message "Reducing data rank to X" is output, and the number of sensors
> represented in the input evoked data is reduced to this number. Disregarding
> the first message, this option appears to control doing temporal PCA on the
> sensor data?

pca does a spatial PCA. Doc says:

    pca: bool
        If True the rank of the data is reduced to true dimension.

but maybe we can it clearer.

so if the rank of the data is 80 the optimization is run on in dimension 80
not 204 for example if you use gradiometers. It's just to save computation
time and should not have any effect on the numerical results.

> This is very tempting since the sensors likely represent an
> oversampling in space, but doesn't this require (temporal) stationarity of
> the data?

with L21 MxNE you can also run a temporal PCA to save time. This is
what the time_pca parameter does. But it does not work with TF-MxNE
as it would break the temporal structure of the data.

HTH
ALex
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