Hi Chris,
2014-12-15 13:42 GMT+01:00 Christopher Bailey <cjb at cfin.au.dk>:
Hi all,
I want to be sure I understand this discussion, as it seems highly
relevant:
- on reduced-rank data {SSS, ICA, ...}, mne.cov.regularize has been the
only way to deal with the loss of dimension
Only to some extent. the current mne.cov.regulrize code takes into account
SSP vectors and applies the diagonal scalar regularization on the low rank
(after applying SSPS). The MNE inverse code seems rank aware to some
extent, more precisely it will discard covariance eigenvalues that are
smaller than required by a certain constant. The idea would be to
parametrize this behaviour by the exact rank instead.
- now Engemann and Gramfort (in press) propose alternatives and implement
an automatic "optimal" cov-regularizer in WIP 1671
Yes. Some of the methods that are implemented there yield estimates of the
rank of the noise namely PPCA and Factor Analysis. These methods regularize
by computing the covariance in the low rank component space and bridging
the gap by adding a noise model.
However, for now this only seems to properly work if the data is not
already SSSed or SSPed or ICAed, probably due to numerical problems. These
options will therefore be deactivated if projs are active. We still have
not fully decided about how to deal with projs for the remaining
alternative methods, i.e. shrunk covariance and Ledoit-Wolf. We will
probably implement a behavior that mimics the one of mne.cov.regularize.
For the whitening examples it would be preferable to divide by the true
rank instead of the number of channels when computing the whitened GFP.
This should be added as an option to mne.cov.whiten_evoked to allow for
most appropriate diagnostic plots.
What advantage, before or now, is there then of exposing the rank
parameter? Would there be an advantage of regularizing in a rank-aware
fashion (which mne.cov.regularize is not, as far as I can tell)?
As I see it, this mostly matters for the whitening step that is part of
many inverse solution techniques such as MNE and beamformers. Scaling
issues might be the result. But since this was already handled by the
existing whitening code I would not be too worried. I bet the existing
constant will most probably lead to handling the SSS rank correctly.
@mluessi When you say "the detected rank is 200", what calculation is
this based on? mne.cov.rank?
The most intuitive approach is plotting the cov and inspecting its eigen
spectra:
http://martinos.org/mne/stable/auto_examples/plot_estimate_covariance_matrix_baseline.html#exa
mple-plot-estimate-covariance-matrix-baseline-py
Note the kink for magnetometers that have 3 SSPs in the sample data set.
We also have a method to the Raw object that's called .estimate_rank
HTH,
Denis
/Chris
--
Christopher Bailey, MSc
MEG Engineer, MINDLab Core Experimental Facility
Center of Functionally Integrative Neuroscience (CFIN)
Aarhus University, Denmark
tel. cell: +45-2674-9927
tel. office: +45-7846-9942
hi,
yes the manual setting of the rank has not be exposed to users in
MNE-Python. This should be fixed to address the use case martin
reported.
@mluessi do you give it a shot?
Alex
Hi Martin to me this makes absolutely sense. This is also related to PR
#1671 on MNE-Python. Likewise, we could have a rank parameter for spatial
whitening.
+1 for taking into account the rank (due to sss | proj | ica)
Hi,
The mne_inverse_operator command has an option to specify the rank of
the covariance matrix. This is useful when e.g. maxfilter has been
applied to the data and the rank has been reduced. It seems like this
option is currently missing from MNE-Python; we are computing an inverse
operator using a covariance matrix estimated from data with maxfilter
applied but the detected rank is 200 (only gradiometers) instead of 64.
Is there a reason for not having this option in MNE-Python or would it
make sense to add it?
Best,
Martin
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