[Mne_analysis] running artifact rejection and then linear regression

[Roberto Petrosino]

Hi all,

I am trying to run linear regression on epochs after artifact rejection. 

Each epoch in my data refers to a specific stimulus (say, a specific word) having specific predictors (i.e., frequency values) associated with it. I have constructed a design matrix with all the predictors I would like to run regression against for each epoch. Since each stimulus will have specific values for the predictors, I assume that if I reject all bad epochs before running regression, there will be a mismatch between the dimension of the data to be regressed and the dimension of the design matrix array. 

So, my question is: is there any way around this - e.g., is there a way have bad epochs only marked as bad, and run linear regression on good epochs only? That way, the dimension of the data to be regressed and the design matrix are the same, but the actual regression calculations will be run selectively. I know that the function linear_regression_raw has the arguments reject and flat that would allow me to do what I want, but I’d actually rather use linear_regression on already epoched and ICA-corrected data, but I don’t seem to find any similar option that would suit my case.

Many thanks in advance,

-Roberto

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Roberto Petrosino
Ph.D. Student in Linguistics
CT Institute for the Brain and Cognitive Sciences
University of Connecticut


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