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Description
I tried to do a time-frequency permutation_cluster_test over 126 channels for different observations and for two groups.
So I have 4D data (observations, times, frequencies, channels) for two groups of users but it didn't work.
adjacency = sparse.csr_matrix(np.zeros((126,126)))
sample = np.zeros((4,100,6,126))
print(sample.shape)
print(adjacency.shape)
F_obs, clusters, cluster_pv, H0 = mne.stats.permutation_cluster_test(X=[sample, sample], adjacency=adjacency)the error raised is
ValueError: adjacency (len 126) must be of the correct size, i.e. be equal to or evenly divide the number of tests (75600). If adjacency was computed for a source space, try using the fwd["src"] or inv["src"] as some original source space vertices can be excluded during forward computation
at line 606 of "mne/stats/cluster_level.py" file.
(and 75600 is evenly dividable by 126, but "got_times" is different from "n_times" for the dimension more)
It seems like at most there can be three dimensions and not four.
I thought that with this version it was possible to do it.
Is it not?
Best,
Giovanni
Originally posted by @GiovanniGr in #7915 (comment)