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| From continuous tutorials: === Noise covariance matrix === To estimate the sources properly, we need an estimation of the noise level for each sensor. A good way to do this is to compute the covariance matrix of the concatenation of the baselines from all the trials in both conditions. * Select at the same time the two groups of trials (right and left). To do this: hold the Control (or Cmd on Macs) key and click successively on the Right and the Left trial lists. * Right-click on one of them and select: Noise covariance > Compute from recordings. Set the baseline to '''[-104,-5] ms''', to consider as noise everything that happens before the beginning of the stimulation artifact. Leave the other options to the default values. Click on Ok. * This operation computes the noise covariance matrix based on the baseline of all the good trials (199 files). The result is stored in a new file "Noise covariance" in the ''(Common files)'' folder. {{attachment:noisecov.gif}} |
Tutorial 14: Noise covariance
Authors: Francois Tadel, Elizabeth Bock, John C Mosher, Sylvain Baillet
Contents
From continuous tutorials:
Noise covariance matrix
To estimate the sources properly, we need an estimation of the noise level for each sensor. A good way to do this is to compute the covariance matrix of the concatenation of the baselines from all the trials in both conditions.
- Select at the same time the two groups of trials (right and left). To do this: hold the Control (or Cmd on Macs) key and click successively on the Right and the Left trial lists.
Right-click on one of them and select: Noise covariance > Compute from recordings. Set the baseline to [-104,-5] ms, to consider as noise everything that happens before the beginning of the stimulation artifact. Leave the other options to the default values. Click on Ok.
This operation computes the noise covariance matrix based on the baseline of all the good trials (199 files). The result is stored in a new file "Noise covariance" in the (Common files) folder.
![[ATTACH]](/brainstorm/Tutorials/NoiseCovariance?action=AttachFile&do=upload_form&ticket=0067d4e460.43cf8dd3de404f22f8676b3927e4366cdf3af6f5&target=noisecov.gif "[ATTACH]"){kind=small}