In an EEG study, I am trying to compare the brain networks of 3 motor tasks while functional interactions are measured by 3 estimators coherence (Coh), imaginary part of coherence (iCoh) and partial coherence (pCoh). My results show that for task 1, more connections are achieved by Coh (denser network), for task 2 more connections are achieved by iCoh and for task 3 more connections are achieved by pCoh. Regardless of other steps and methodological considerations of this research, is there any explanation for this behavior of connectivity estimators? Is there any reference regarding this issue?
You are confusing some concepts here. An estimator is a function of a sets of samples from a distribution. Estimators have different properties, like been biased, consistency, etc. (https://en.wikipedia.org/wiki/Estimator). Estimators are not scalar quantities.
From your question it is unclear what you mean with estimator. Maybe you're asking why did you get different results for each of the three conditions? If that's your question, you will have to provide details about your paradigm, etc.. However, it is your task as experimenter to interpret your results.
Thanks for your answer.
Brain connectivity estimators measure the pattern of interactions (functional/effective) in the brain. https://en.wikipedia.org/wiki/Brain_connectivity_estimators
There are several connectivity estimators and in my question I asked about the behavior of Coh, iCoh and pCoh. Since I am dealing with 3 motor tasks with similar nature, I expected to see for example more connections are obtained by Coh for all 3 tasks compared to the network estimated by iCoh or pCoh. However, I've seen something else as explained above. I couldn't find any reference regarding such issue.
Hi,
If you are using EEG in sensor level then the most robust is the iCoh because it is not vulnerable to volume conduction.
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