[Mne_analysis] noise covariance matrix

Lucy MacGregor Lucy.MacGregor at mrc-cbu.cam.ac.uk
Thu Apr 6 06:29:42 EDT 2017
Search archives:


Dear MNE users,

I would very much appreciate your advice on the results I am getting from calculation of the noise covariance matrix. I'm using the "method" option for mne.compute_covariance to do automated regularisation.

Data were collected with Neuromag 306 Vectorview system. My responses are time locked to the onset of the average of ~300 auditorily-presented sentences. I have used the silent (baseline) period -500-0ms before sentence-onset as the time period from which to estimate the noise.

##################
event_id = None
tmin, tmax = -0.5, 5.5
reject_tmin, reject_tmax = -0.5, 1.5
bmin, bmax = -0.5, 0

epochs = mne.Epochs(raw, events, event_id, tmin, tmax, reject_tmin = reject_tmin, reject_tmax = reject_tmax, picks=picks, baseline=baseline, reject=reject, preload=True, add_eeg_ref=True)
noise_cov = mne.compute_covariance(epochs, method =('shrunk', 'empirical'), tmin=bmin, tmax=bmax, return_estimators = True)
###################

The plot below is for a single subject (but all my subjects show similar-looking output) for a period -500 to 5000ms covering the duration of my sentences.

I have compared my output with that for the examples:
http://martinos.org/mne/stable/auto_examples/visualization/plot_evoked_whitening.html#sphx-glr-auto-examples-visualization-plot-evoked-whitening-py
http://martinos.org/mne/stable/auto_tutorials/plot_compute_covariance.html

The result tells me that "shrunk" is the best method, but from looking at the output from whitening I'm unsure how this is the case, and in fact whether either method is working as it should.

[cid:image003.jpg at 01D2AEC9.159B1430]


Evoked signals for all channels:
For the MEG, during the baseline the values are generally within the +/- 1.96 indicated by the red dotted line, so I think this is OK.
Data look quite noisy

GFP plots for MEG:
For 'empirical', the baseline values > 1 whereas for 'shrunk, the baseline values <1. As I understand it values should be around 1 and therefore both methods look problematic.

My question is therefore:
when the baseline GFP is > or < 1 then is this due to problems with regularisation and where should I go from here?


With thanks for your thoughts and advice.

Kind regards,

Lucy MacGregor

-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20170406/facb18ff/attachment-0001.html 
-------------- next part --------------
A non-text attachment was scrubbed...
Name: image003.jpg
Type: image/jpeg
Size: 37936 bytes
Desc: image003.jpg
Url : http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20170406/facb18ff/attachment-0001.jpg 


More information about the Mne_analysis mailing list