Not sure if this belongs here:
I want to use
mne.preprocessing.corrmap for selecting EOG/EKG Independent Components. AFAIK
corrmap computes some sort of average IC based on a seed IC (template) given by the user. It is assumed that the average template is a better template for matching similar components in other ICA decompositions.
Here is the problem:
A training and a test dataset exists. I want to calculate the average template only with the training data and apply it to the test data. How should I do that? (I am thinking of the
For now I intend to run corrmap twice:
- Only with training data. → Remove artifactual ICs in training dataset
- With training and test data → Remove artifactual ICs from test dataset, but compute an average template that uses both the training and test dataset.
There is a small chance that other ICs are identified, when the average template is build from training and test data. This could create problems for testing a model which was build with the training data. (I don’t think it is a big problem in my case, since I have more then 500 training recordings. But when only 10 training subjects exist, then the average template would change when a new test subject was added. Tbh. one can manually identify ICs in the last case.)