Cluster Permutation Analysis with mne.stats.spatio_temporal_cluster_test

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1

Hello its me again!

I have a rather general question regarding how to apply the spatio_temporal_cluster_test correctly and how to interprete the results of it. I am just starting in the EEG field so it might be a rather simple question for the experts here.

Here is my code:


# Concatenate the data across conditions
X = [condition_a_2D, condition_b_2D]  # List of arrays, shape: (n_epochs, n_times, n_channels)


n_permutations = 1000  # Number of permutations for the permutation test
tail = 0  # Two-sided test
seed = 42

# Perform spatio-temporal clustering
F_obs, clusters, p_values, _ = spatio_temporal_cluster_test(X, n_permutations=n_permutations, tail=tail, seed=seed, adjacency=adjacency)

I have EEG data inserted for X with the dimensions 41 trials, 4401 timepoints and 64 channels. It used to be a time frequency structure with the dimensions 41, 4401, 5, 64. 5 have been the frequency bins but I averaged over them.

Now I am not sure how to correctly use the mne function. I think I also need to specify a t-test as statistics with a threshold? What condition must be put on position one and two of X? I have a control and intervention condition.

Then how to interprete the outcomes of the function?

And when I have results for one subject with the two conditions how can I analyse the two conditions over all my participants in the experiment?

Best regards,

Till

2

if you have just two conditions, then you could also subtract them from each other and calculate a one sample ttest against zero, see: mne.stats.spatio_temporal_cluster_1samp_test — MNE 1.7.0.dev5+g44c787fd4 documentation

That’s a bit out of scope for this support forum. I suggest you read up on academic references for this (for example: Nonparametric statistical testing of EEG- and MEG-data - PubMed) or look at the MNE-Python examples, for example: Permutation t-test on source data with spatio-temporal clustering — MNE 1.7.0.dev5+g44c787fd4 documentation

For that you take the analysis to the “second level” → instead of analyzing the epochs in a participant, you average over epochs in each participant, and then analyze over participants. That is the n_epochs becomes n_participants.

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3

Hello Stefan,

thank you for your response. I am now trying to use the 1 samp test function you provided. I summarized the participants for the second level and want to calculate the difference between the two conditions resulting in a 20 participants x 64 channels x 5 frequencies x 4401 timepoints.

I get how the theory of the permutations works for 2 conditions as the input. How is it done with this one difference condition?

Best regards,

Till

4
  • Assuming that this is a within-subjects design, each participant has contributed conditions A and B.
  • You are supplying their difference to a test against zero: diff = A-B
  • The permutation is about changing the labels (A to B and B to A, or leaving them as they are; randomly) within participants
  • This is the same as changing the sign of diff for each participant
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