Here we will refer to each spatial location as a \"voxel\".\n In general, though, it could be any sort of data value,\n including cortical vertex at a specific time, pixel in a\n time-frequency decomposition, etc.

In the case of a true one-sample t-test, i.e. analyzing a single\n condition rather than the difference between two conditions,\n it is not clear where/how exchangeability applies; see\n `this FieldTrip discussion

In most MNE functions, data has shape\n ``(..., n_space, n_time)``, where the spatial dimension can\n be e.g. sensors or source vertices. But for our spatio-temporal\n clustering functions, the spatial dimensions need to be **last**\n for computational efficiency reasons. For example, for\n :func:`mne.stats.spatio_temporal_cluster_1samp_test`, ``X``\n needs to be of shape ``(n_samples, n_time, n_space)``. You can\n use :func:`numpy.transpose` to transpose axes if necessary.