- MNE-Python version: 0.23.0
- operating system: Windows 10
Dear MNE users and maintainers,
I have read conflicting information about whether to perform ICA on cleaned/epoched data or directly to the raw data. I get the impression that it is highly depended on a number of factors including the data type, artifacts, and goal of the project.
Some discussion on this:
I do not know what the best approach is for my data (EEG only). I’ve been focusing on fitting ICA to cleaned/epoched dataset and assumed that this was fine, but (out of curiosity) I just fitted ICA to some of my raw data and it seems that it might perform better.
My case is a little different as I am not just looking at removing eye blinks or other common artifacts, but I have short (~500ms) external electrical stimulation events (tES) that I want to remove. I am using the FastICA algorithm with the default parameters.
I was wondering if anyone had experience with applying ICA to remove short tES artifacts (or something similar), could give me some feedback based on their experience particularly regarding the workflow for preprocessing.
Also, more generally, if someone could recommend a good method for comparing or evaluating the efficacy of different ICA fittings on the same dataset (or comparing raw vs epoched versions of the same dataset), I would be very interested and grateful for your input.
Finally, I’ve been a little confused about the baseline correction and filtering warning that mne generates when I apply ICA to my datasets. Do these warning suggest that there is a specific order of operations that I should always follow regardless of raw/epoched dataset prior to fitting to ICA?
Thank you all for your assistance,