Hi and thanks for the past answers to everybody.
My question may seem strange, but maybe you can help to make it clearer.
One professor suggested me to model the EEG data with a linear
regression. The formulas he provided are VERY similar to the SPM
analysis (y = a + bx). So, we get beta values indicating the impact of
a certain factor on the raw noisy signal. I am aware this is not
exactly what SPM does, we don't have a BOLD signal to predict EEG. The
idea was just to do a linear regression on each time point (i.e. each
millisecond).
But I think the problem is different. From various presentations the
BOLD signal grows visibly after a single trial, while this never
happens within EEG. That is, the signal to noise ration is much better
in fMRI, compared to EEG. If this is true fMRI would need less trials
to get a significant result.
Am I right assuming this? Is EEG signal more noisy than fMRI?
Hope this is not a stupid question. Just wondering if fMRI methods can
be transferred to EEG analysis.
Thank you.
Dorian.
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