[Mne_analysis] specify noise covariance rank in MNE-Python?

[Christopher Bailey]

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
- now Engemann and Gramfort (in press) propose alternatives and implement an automatic "optimal" cov-regularizer in WIP 1671

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)?

@mluessi When you say "the detected rank is 200", what calculation is this based on? mne.cov.rank?

/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

On Dec 11, 2014, at 3:03 PM, Alexandre Gramfort <alexandre.gramfort at TELECOM-PARISTECH.FR<mailto:alexandre.gramfort at TELECOM-PARISTECH.FR>> wrote:

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


On Thu, Dec 11, 2014 at 1:03 PM, Denis A. Engemann
<denis.engemann at gmail.com<mailto:denis.engemann at gmail.com>> wrote:
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)

On 11 Dec 2014, at 12:47, Martin Luessi <mluessi at nmr.mgh.harvard.edu<mailto:mluessi at nmr.mgh.harvard.edu>> wrote:

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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