Understanding Source Localization of bst_inverse_linear[2018]

[Sylvain]

@unes1111

• is the observed Lead Field matrix “L” = multiplication of the noisy data (M) by the head model (grid points) (in matrix gain function) and then whitened and weighted?

The lead field matrix is another name for the gain matrix produced by the forward model.

@unes1111

• How is the Kernel equation : Kernel = Lambda * L’ * (UL * diag(1./(Lambda * SL2 + 1)) * UL’) drived?

It is a computationally optimized version of the MNE equation you are quoting in #3. @jcmosher can tell you more about how it is derived more specifically.

@unes1111

4. Assuming that we have the kernel image, what function has been used to illustrate the sources on the head surface? (Demonstration Function? )

The kernel is not an image, it is a matrix operator applied to the sensor data to produce an estimate of source time series at every cortical location. It is the large matrix multiplication to the left of M (the sensor data) in the equation in your point #3.

Hope this clarifies.