Comparison between original EEG and source activity signal

[Davi1990 (Dave)]

Dear MNE expert,

I’ve computed the source activity and now I want to bring it back to channels space. For this purpose, I’ve run the following code:

EEG = mne.apply_forward(fwd_fixed, stcs, real_EEG_epoched.info).data 

However, if I plot the original EEG data (the one that I used to compute the source activity) with the data that I got from this transformation (source activity to channels space) they look different. In the following image I’ve plotted in red a channel from the original EEG and in blue the same channel obtained by this transformation

Given the nature of the inverse problem,

M = GX + E

I was wondering whether if it’s expected that the two signals are different. If yes, why is that?

Is there a way to retrieve the original EEG signal from its source estimation?

Thanks in advance

[agramfort (Alexandre Gramfort)]

I see 2 reasons for the differences:

• the inverse problem can use the dipoles non normal to the cortex to explain the data
• you regularized a lot in the inverse problem with a strong lambda2

Alex

[Davi1990 (Dave)]

@agramfort

lambda2 was default. I’ve seen someone has the same issue in the past:

Forward/Inverse modeling sanity checks

Objective I’m playing around with forward/inverse modeling in MNE and am trying to check the assumption: (1) kernel @ gain == I Where kernel is the prepared inverse operator kernel, gain is the leadfield matrix, and I is an identity matrix. By extension, I want to check that: (2) x == kernel @ gain @ x Where x is the original time series data. (2) is easier to check due to how MNE stores the leadfield matrix, but I need to obtain kernel and gain that satisfy (1) as well. Assumptions f…

Given the reasons for the differences you listed, any suggestions on how to retrieve the original EEG signal from a practical point of view?

Thanks