<div dir="ltr">I forgot to mention, if you also have EEG data, you cannot use an empty room noise cov.</div><div class="gmail_extra"><br><div class="gmail_quote">2014-10-01 16:31 GMT+02:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Baptiste,<div><br></div><div>If you have classical ERFs and a 'baseline' I would not rule out computing the noise cov from baseline segments, In my experience inverse solutions based on such a 'subject' noise covariance are often more focal. I had cases where analyses would have failed using an empty room noise cov.</div><div>I share your intuition about the classification of the noise covariances you have sent.</div><div>Very roughly you can say that a covariance is better if its matrix plot looks more diagonal.</div><div>As the covariance is used for whitening the data (sensor data + lead field) you can investigate its quality by computing a whitener and applying it to the data:</div><div><br></div><div><a href="http://martinos.org/mne/stable/auto_examples/plot_evoked_whitening.html" target="_blank">http://martinos.org/mne/stable/auto_examples/plot_evoked_whitening.html</a><br></div><div><br></div><div>If the majority of signals in the baseline (assumed to represent signals of non-interest) are not within -1.96 and 1.96 something is wrong. The cov is actually good if the covariance matrix of the whitened signals looks like an identity matrix.</div><div><br></div><div>Regularization is important when the number of samples used to compute the noise cov is small.</div><div>But it's also important combine different sensort types.</div><div><br></div><div>C.f. <a href="http://martinos.org/mne/stable/auto_examples/inverse/plot_make_inverse_operator.html#example-inverse-plot-make-inverse-operator-py" target="_blank">http://martinos.org/mne/stable/auto_examples/inverse/plot_make_inverse_operator.html#example-inverse-plot-make-inverse-operator-py</a></div><div><br></div><div><br></div><div>HTH,</div><div>Denis</div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="h5">2014-10-01 16:02 GMT+02:00 Baptiste Gauthier <span dir="ltr"><<a href="mailto:gauthierb.ens@gmail.com" target="_blank">gauthierb.ens@gmail.com</a>></span>:<br></div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div><div class="h5"><div dir="ltr"><div><div><div><div><div><div>Dear mne-python experts and users,<br><br></div>following
the guidelines of source reconstruction of ERFs, I estimated noise
covariance matrices from empty room noise (neuromag system) for
calculating inverse operator. When looking at the source estimates I
got, it appears that source amplitude can be very variable, not in term
of timecourse patterns (which is good for ERFs) but in term of absolute
amplitude (need to play with "fmult" in mne_analyze visualization tools;
I suppose it's bad for stats).<br></div>So I checked if the noise
estimation was similar across subjects and realize I have no criterion
to decide if noise covariance was "ok" or not... <br></div>What criterion should I apply?<br></div>Should I use then regularization for "bad" subjects?<br></div><div><br></div>PS:find attached several noise covariance matrices from my study<br></div>PPS:
Does it make sense to band-pass the empty room signal with the same
classical band pass applied to the data? Can it improve a bit the thing?<br><br clear="all"><div>Best,<br><br></div>Baptiste Gauthier<br><br><br><br><div class="gmail_chip gmail_drive_chip" style="width:396px;min-height:18px;max-height:18px;padding:5px;color:rgb(34,34,34);font-family:arial;font-style:normal;font-weight:bold;font-size:13px;border:1px solid rgb(221,221,221);line-height:1;background-color:rgb(245,245,245)"><a href="https://docs.google.com/file/d/0B_eZxstAMJQscGpiOF9VY00yLWc/edit?usp=drive_web" style="display:inline-block;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;text-decoration:none;padding:1px 0px;border:medium none;width:100%" target="_blank"><img style="vertical-align:bottom;border:none" src="https://ssl.gstatic.com/docs/doclist/images/icon_11_image_list.png"> <span dir="ltr" style="color:rgb(17,85,204);text-decoration:none;vertical-align:bottom">bad?.png</span></a></div><br><div class="gmail_chip gmail_drive_chip" style="width:396px;min-height:18px;max-height:18px;padding:5px;color:rgb(34,34,34);font-family:arial;font-style:normal;font-weight:bold;font-size:13px;border:1px solid rgb(221,221,221);line-height:1;background-color:rgb(245,245,245)"><a href="https://docs.google.com/file/d/0B_eZxstAMJQsY01WdGlJbENHa0U/edit?usp=drive_web" style="display:inline-block;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;text-decoration:none;padding:1px 0px;border:medium none;width:100%" target="_blank"><img style="vertical-align:bottom;border:none" src="https://ssl.gstatic.com/docs/doclist/images/icon_11_image_list.png"> <span dir="ltr" style="color:rgb(17,85,204);text-decoration:none;vertical-align:bottom">good?.png</span></a></div><br></div><div class="gmail_extra"><br><div class="gmail_quote">2014-10-01 14:05 GMT+02:00 Baptiste Gauthier <span dir="ltr"><<a href="mailto:gauthierb.ens@gmail.com" target="_blank">gauthierb.ens@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div><div><div><div><div>Dear mne-python experts and users,<br><br></div>following the guidelines of source reconstruction of ERFs, I estimated noise covariance matrices from empty room noise (neuromag system) for calculating inverse operator. When looking at the source estimates I got, it appears that source amplitude can be very variable, not in term of timecourse patterns (which is good for ERFs) but in term of absolute amplitude (need to play with "fmult" in mne_analyze visualization tools; I suppose it's bad for stats).<br></div>So I checked if the noise estimation was similar across subjects and realize I have no criterion to decide if noise covariance was "ok" or not... <br></div>What criterion should I apply?<br></div>Should I use then regularization for "bad" subjects?<br></div><div><br></div>PS:find attached several noise covariance matrices from my study<br></div>PPS: Does it make sense to band-pass the empty room signal with the same classical band pass applied to the data? Can it improve a bit the thing?<br><div><br clear="all"><div><div><div><div><div><div>Best,<br><br></div><div>Baptiste Gauthier<span><font color="#888888"><span><font color="#888888"><br><br>-- <br>Baptiste Gauthier<br>Postdoctoral Research Fellow<br><br>
INSERM-CEA Cognitive Neuroimaging unit<br>
CEA/SAC/DSV/DRM/Neurospin center<br>
Bât 145, Point Courier 156 <br>
F-91191 Gif-sur-Yvette Cedex FRANCE
<div style="display:inline"></div>
</font></span></font></span></div></div></div></div></div></div></div></div><span><font color="#888888">
</font></span></blockquote></div><span><font color="#888888"><br><br clear="all"><br>-- <br>Baptiste Gauthier<br>Postdoctoral Research Fellow<br><br>
INSERM-CEA Cognitive Neuroimaging unit<br>
CEA/SAC/DSV/DRM/Neurospin center<br>
Bât 145, Point Courier 156 <br>
F-91191 Gif-sur-Yvette Cedex FRANCE
<div style="display:inline"></div>
</font></span></div>
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