Hi everyone,

I have a question about the Brainstorm implementation of the LCMV beamformer that I'm hoping perhaps @John_Mosher or others might be able to clarify.

It appears that the noise covariance (e.g., derived from an empty room) may not be taken into account in the beamformer computation - is this correct? I've tried swapping noise covariance matrices between files, and the source images and kernel appear to be identical.

In "Tutorial 22: Source estimation -> Advanced options: LCMV beamformer", there appears to be conflicting information. In the opening paragraph it is stated that "...on top of the noise covariance matrix, you need to estimate a data covariance matrix in order to enable the option 'LCMV beamformer' in the interface", which I have found to be true. However, just below it also states that the PNAI is modified "to rely strictly on the data covariance, without need for a separate noise covariance matrix" and that regularization is only applied to the data covariance matrix.

In looking at the code (bst_inverse_linear_2018.m) I also see many indications that this may be the case:

-line 466: "We only calculate the data covariance matrix for LCMV"

-line 1059-60: "in the lcmv case, we used the data covariance as the whitener for programming simplicity"

If the noise covariance is not necessary for the LCMV computation, why would it be necessary to enable this option in the source imaging menu? Or is it used somewhere in the computation that I am missing?

Thanks in advance for any guidance,

Alex