[Mne_analysis] Permutation cluster test for repeated measures

amir Djalovski amir.djv at gmail.com
Wed May 22 05:13:31 EDT 2019
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Dear all,

*Background* - I conducted an EEG hyper-scanning experiment with 3
experimental groups and two repeated measures.
In order to examine the connectivity between the participants, I
concatenated both participants' caps (31 channels each) to be one 62
channel cap.
For each repeated task, using the function
calculated the connectivity between the participants.

*My question* - I want to run a permutation cluster test for groups (3
categorise) * repeated measures (before/after).
I'm using the function mne.stats.permutation_cluster_test
How can I adjust the code below to run repeated measures?

for f_idx, f_lab in enumerate(frequencies_labels):
    print(f_idx, f_lab)
    group1= vectconn[np.argwhere(grps==1), f_idx, :]
    group2= vectconn[np.argwhere(grps==2), f_idx, :]
    group3= vectconn[np.argwhere(grps==3), f_idx, :]

    Fobs, clusters, cluster_pv, H0 =
mne.stats.permutation_cluster_test(X=[group1, group2, group3],


threshold = threshold,

n_permutations = 1024,

seed = 42,

step_down_p = pvalue,

connectivity = lil_matrix(clusters),

check_disjoint = False)

vectconn is my data.

The shape for each group is:
Out[44]: (26, 1, 961)

Out[45]: (27, 1, 961)

Out[46]: (18, 1, 961)

Thus, for example, for the first group I have 26 couples and I have 961
(31**2 , number of  between participants connectivity measures). The 1
reflects that I'm examining only one frequency. I'm running the same
permutation test in a loop over alpha, beta, and gamma.

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