Trying to understand the lambda2 parameter in mne.minimum_norm.apply_inverse_raw

[drammock (Dan McCloy)]

Ah, OK, I should have read your question more closely. I think your’e right when you say that

@neurothew

I think I kind of understand what happened. If I am setting the noise covariance to a ad-hoc matrix with 0.2uV std for each channel, essentially the whitening just divide each channel by the std, thus making the amplitude fluctuation much larger.

I think the question to ask is why do you want/need to whiten the data? If I understand correctly, using an ad-hoc covariance with default settings will not actually do any whitening (because it’s a diagonal matrix with identical values along the diagonal, so it won’t capture any actual covariance between the channels, nor will it account for differences in noise level between channels). So if you actually need the data to be whitened you’ll need to either (1) pick a “baseline” period (might be arbitrary, or maybe there are reasons to prefer early, middle, or late portions of the recording?) or (2) find some way to create an ad-hoc covariance that is at least somewhat better than the default settings (maybe by doing some cross-subject aggregation of data covariances?)

But since I’m not an expert in resting-state I think @larsoner’s comment in the other thread is the best advice:

@larsoner

I’d recommend looking at recent resting-state EEG papers to see what they use as a noise covariance. I’d expect most papers with source localization to have some definition of a noise covariance matrix.