[Mne_analysis] tf_mixed_norm and PCA option?

[Alexandre Gramfort]

hi Per,

>     First, as your publications mention, the model requires that the noise terms be Gaussian white noise. The whitening process is based on the noise covariance matrix and is designed to decorrelate the noise between different sensor channels and then to standardize the variance of these noise distributions. The noise covariance matrix itself is derived from either empty room measurements or combined periods of pre-stimulus measurement with the subject. In either case, these are resting states where we can assume the state of the system is stable and therefore can estimate these covariances over time.

yes exactly.

>     Second is my question about the pca flag to tf_mixed_norm: is this doing a spatial pca on the post-stimulus data itself, or is this transformation also estimated from the pre-stimulus data? In the former case, I am concerned about stationarity (because the brain network is in a transient, dynamic state), but in the later it makes sense.

please have a look at this code:

the TF-MxNE line that uses pca param:
https://github.com/mne-tools/mne-python/blob/master/mne/inverse_sparse/mxne_inverse.py#L372

the function to whiten the data that is the same as the one used in
MNE/dSPM/sLORETA
https://github.com/mne-tools/mne-python/blob/master/mne/minimum_norm/inverse.py#L1083

the computation change between pca = True or False.
https://github.com/mne-tools/mne-python/blob/master/mne/minimum_norm/inverse.py#L1111

basically it just projects out of the space associated with the tiny
eigen values of the noise cov.

so there is no time involved and stationarity issues. Unless you
consider the noise cov to
vary with time but then you have a different model.

clearer?

Alex