When using minimum norm methods for source estimation (in the case of EEG), which is to say going from the signal at the recording sites on the scalp to the signals in source space, i.e. equivalent dipoles for instance in an idealized cortical surface, a noise covariance matrix is often needed.
First of all, how is it computed? From what I have read, for instance in the MNE-Python package (source), the data is epoched into equal length segments, then the "covariance is computed". What is the precise mathematical formula applied to those epochs?
Second of all, this covariance matrix is said to be a measure of the reliability of the sensors. Why is that?