# Statistic analysis on EEG data - how to deal with it properly?

Let's assume that I have a datasheet looking like that:

Patient Electrode  Task Theta power
1       1          1    0.5
1       2          1    0.8
1       3          1    0.1
1       4          1    0.9
...
1       1          2    0.3
1       2          2    0.9
1       3          2    0.4
1       4          2    0.3
...
2       1          1    0.2
2       2          1    0.7
2       3          1    0.3
2       4          1    0.4
...
2       1          2    0.6
2       2          2    0.7
2       3          2    0.8
2       4          2    0.8
...
3       1          1    0.0
3       2          1    0.3
3       3          1    0.2
3       4          1    0.8
...
3       1          2    0.2
3       2          2    0.5
3       3          2    0.1
3       4          2    0.9


I have for example 20 patients, each of the patient has 128 electrodes recorded from one experiment with 2 categories. So data for the patients (for example this theta power on each electrode) are INDEPENDENT, because the data are from different people, and from only one run of the experiment. But on the other hand the data regarding electrodes from each patient are DEPENDENT, all electrodes share a bit of common signal.

What kind of statistical tests we can use to check the difference in for example this theta power between the task no. 1 and no.2? How to take care of this dependancy/independancy in neuroscience data?

• I will answer my own question then. I found intresting article on this topic. I encourage everyone who has the same problem to read that one ("Statistical testing in electrophysiological studies", Eric Maris, Psychophysiology, onlinelibrary.wiley.com/doi/epdf/10.1111/…) – Mary May 23 '18 at 8:59
• This is a very general statistical question and would be better posted on cross-validated. Now that you have already answered your own question, feel free to post what you found as an actual answer (not a comment) and then accept it. – S.A. Jun 2 '18 at 10:58