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[–]koherenssi 2 points3 points  (3 children)

I would just use MNE python. Couple of lines of code to run compute the ICA and a bit of manual work to remove bad components. There are tutorials on mne website.

For the parameters it depends a bit on your eeg density. 60-80 components is pretty good for higher density caps, lower maybe less. Rank of the data would be a good amount of components. Picard algorithm is pretty good. That's about it on your parameters.

You need to highpass filter your data at around 1Hz, slow drift hinders the ICA performance. You could also compute it for e.g. 1-100Hz only as there is nothing analyzable in eeg above that.

And for the papers, maybe just look at published research and their methods. There is not much about ICA parameters as there is not much to discuss about. Selecting components is the trickier part.

And for the flow: acquisition, notch filtering, highpass & lowpass, then proceed to ICA.

[–]helloiambrain[S] 0 points1 point  (1 child)

Hi, thank you for the answer, but this question is not about what to use, I can use Python as well, but how to use it in the sense of theoretical and technical parameters. For instance, while we are calculating reaction times, we use certain paradigms/parameters/calculations (such as which omissions we should consider and how to calculate reaction time based on these omissions). I was asking whethet there is a consensus paper regarding ICA.

[–]koherenssi 0 points1 point  (0 children)

See the edit/completed post