[Mne_analysis] tf_mixed_norm and PCA option?

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Tue Sep 30 16:33:06 EDT 2014
Search archives:

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



More information about the Mne_analysis mailing list