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

Hari Bharadwaj hari at nmr.mgh.harvard.edu
Wed Feb 19 00:50:51 EST 2014
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Hi Teon,
     Assuming that your regression is a linear model fit that doesn't operate across channels or across time in any way, the beta values are just linear combinations of sensor space data over groups of trials... So it should be fine...

One thing to keep in mind, if the conditions (i.e., levels of the word frequency factor in your example) have different numbers of trials is to scale the noise covariance by an appropriate number as discussed in the "Effective number of averages" section of the "the current estimate" chapter of the manual...


Hari Bharadwaj
PhD Candidate, Biomedical Engineering,
Auditory Neuroscience Laboratory
Boston University, Boston, MA 02215

Martinos Center for Biomedical Imaging,
Massachusetts General Hospital
Charlestown, MA 02129

hari at nmr.mgh.harvard.edu
Ph: 734-883-5954

> On Feb 19, 2014, at 12:34 AM, Teon Brooks <teon at nyu.edu> wrote:
> 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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