[Mne_analysis] Computing regression on sensor data then transforming to source space

[Teon Brooks]

Hi MNE listserv,

I have single-trial data that I would like to regress a predictor (let's
say word frequency) on it and then compute a source estimate. I'm planning
to use mne-python to do this computation. I was wondering if I could do the
regression over single trial sensor data first, get the beta values for
each sensor over time, and then compute the source estimate as if it were
an evoked object.

My presumption is that it should be fine if the source transformation is
linear. The other option would be to source transform the data then do the
regression but the problem with doing this first is that computing the
source estimates is more demanding on memory (say about 1000 trials with
the around 5000 sources over 600-800ms of time). It would be more efficient
if this computation could be done first if it is not computationally ill.

What are your thoughts?

Best,
--
teon
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