I am wondering if anyone who cleans EEG with ICA has an opinion about this. Some researchers set a threshold where EEG datasets with more than a certain number of artifact ICs should be rejected outright. First, I’m wondering what people here think about setting such a threshold, and how strict it ought to be? Second, while QC’ing data, is it better to set a threshold for 1) the number of artifact ICs removed or 2) the proportion of variance explained by those same artifact ICs? In other words, I might have one dataset where I removed 10 ICs, but those ICs only explain 20% of the variance in the original data, whereas in another dataset, I removed 5 ICs, but those ICs explain 60% of the variance in the original data. My intuition would be that it makes more sense to throw out the second dataset than to throw out the first dataset. However, I’m not sure if I’ve ever heard of anyone doing it that way, so maybe the motivation for setting such a threshold is more about ensuring that the cleaned data are not low rank/low dimensional? Does anyone have an opinion on this?
To add a bit of a twist, suppose my analysis is within-subject. Does it make sense to check whether the number of artifact ICs removed differs between treatments/conditions? On the one hand, removing more ICs in one condition than another may bias the results, but on the other hand, one condition might just be inherently nosier than another … Would this question change at all if we swapped “number of ICs removed” with “variance explained by the removed ICs”?
Finally, would it make sense to add either of these two variables (number of ICs removed or variance explained by removed ICs) as a covariate in the analysis? My intuition says probably not, but I’m wondering what opinions folks here have.