Dear Brainstorm contributors,
Let me ask a question about the noise covariance when performing source estimates for resting state EEG data.
Your tutorial on noise covariance is written as "Calculate noise covariance over a long segment of resting recordings".
EEG data are usually epoched (e.g. 4 seconds per epoch) during preprocessing to remove artifacts.
However, I think the epoched data are too short of creating noise covariance.
Therefore, if we consider creating a noise covariance, should we use non-epochizing methods in the preprocessing of the resting EEG? or do you have any other recommended way to create noise covariance for resting-state EEG?
You can use epoched data to compute the noise covariance matrix. Take into account that the required number of samples is N*(N+1)/2 samples, N is the number of sensors.
See the Using multiple continuous blocks point in:
Select the advanced option Diagonal noise covariance when computing sources to use only the diagonal elements of the noise covariance matrix:
Hi @Raymundo.Cassani ,
Thanks for the great advice!
I am very glad to hear that.
May I just confirm one point?
Does "number of samples" refer to the number of time points per epoch?
Nop, it refers to the total number of time points across all epochs:
nEpochs * nSamplesInEpoch
Thank you for your clear and helpful advice.
I would be happy to do so!