mne.stats.permutation_cluster_1samp_test(X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, connectivity=None, verbose=None, n_jobs=1, seed=None, max_step=1, exclude=None, step_down_p=0, t_power=1, out_type='mask', check_disjoint=False, buffer_size=1000)[source]

Non-parametric cluster-level paired t-test.

X : array, shape=(n_samples, p[, q])

Array where the first dimension corresponds to the difference in paired samples (observations) in two conditions. X[k] can be a 1D or 2D array (time series or TF image) associated to the kth observation.

threshold : float | dict | None

If threshold is None, it will choose a t-threshold equivalent to p < 0.05 for the given number of observations (only valid when using an t-statistic). If a dict is used, then threshold-free cluster enhancement (TFCE) will be used, and it must have keys 'start' and 'step' to specify the integration parameters, see the TFCE example.

n_permutations : int | ‘all’

The maximum number of permutations to compute. Can be ‘all’ to perform an exact test.

tail : -1 or 0 or 1 (default = 0)

If tail is 1, the statistic is thresholded above threshold. If tail is -1, the statistic is thresholded below threshold. If tail is 0, the statistic is thresholded on both sides of the distribution.

stat_fun : callable | None

Function used to compute the statistical map (default None will use mne.stats.ttest_1samp_no_p()).

connectivity : sparse matrix | None | False

Defines connectivity between features. The matrix is assumed to be symmetric and only the upper triangular half is used. This matrix must be square with dimension (n_vertices * n_times) or (n_vertices). Default is None, i.e, a regular lattice connectivity. Use square n_vertices matrix for datasets with a large temporal extent to save on memory and computation time. Can also be False to assume no connectivity. Can also be False to assume no connectivity.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

n_jobs : int

Number of permutations to run in parallel (requires joblib package).

seed : int | instance of RandomState | None

Seed the random number generator for results reproducibility.

max_step : int

When connectivity is a n_vertices x n_vertices matrix, specify the maximum number of steps between vertices along the second dimension (typically time) to be considered connected. This is not used for full or None connectivity matrices.

exclude : boolean array or None

Mask to apply to the data to exclude certain points from clustering (e.g., medial wall vertices). Should be the same shape as X. If None, no points are excluded.

step_down_p : float

To perform a step-down-in-jumps test, pass a p-value for clusters to exclude from each successive iteration. Default is zero, perform no step-down test (since no clusters will be smaller than this value). Setting this to a reasonable value, e.g. 0.05, can increase sensitivity but costs computation time.

t_power : float

Power to raise the statistical values (usually t-values) by before summing (sign will be retained). Note that t_power == 0 will give a count of nodes in each cluster, t_power == 1 will weight each node by its statistical score.

out_type : str

For arrays with connectivity, this sets the output format for clusters. If ‘mask’, it will pass back a list of boolean mask arrays. If ‘indices’, it will pass back a list of lists, where each list is the set of vertices in a given cluster. Note that the latter may use far less memory for large datasets.

check_disjoint : bool

If True, the connectivity matrix (or list) will be examined to determine of it can be separated into disjoint sets. In some cases (usually with connectivity as a list and many “time” points), this can lead to faster clustering, but results should be identical.

buffer_size: int or None

The statistics will be computed for blocks of variables of size “buffer_size” at a time. This is option significantly reduces the memory requirements when n_jobs > 1 and memory sharing between processes is enabled (see set_cache_dir()), as X will be shared between processes and each process only needs to allocate space for a small block of variables.

t_obs : array, shape (n_tests,)

t-statistic observed for all variables

clusters : list

List type defined by out_type above.

cluster_pv : array

P-value for each cluster

H0 : array, shape (n_permutations,)

Max cluster level stats observed under permutation.


From an array of paired observations, e.g. a difference in signal amplitudes or power spectra in two conditions, calculate if the data distributions in the two conditions are significantly different. The procedure uses a cluster analysis with permutation test for calculating corrected p-values. Randomized data are generated with random sign flips. See [1] for more information.

Because a 1-sample t-test on the difference in observations is mathematically equivalent to a paired t-test, internally this function computes a 1-sample t-test (by default) and uses sign flipping (always) to perform permutations. This might not be suitable for the case where there is truly a single observation under test; see Statistical inference.

If n_permutations >= 2 ** (n_samples - (tail == 0)), n_permutations and seed will be ignored since an exact test (full permutation test) will be performed.

If no initial clusters are found, i.e., all points in the true distribution are below the threshold, then clusters, cluster_pv, and H0 will all be empty arrays.


[1](1, 2) Maris/Oostenveld (2007), “Nonparametric statistical testing of EEG- and MEG-data” Journal of Neuroscience Methods, Vol. 164, No. 1., pp. 177-190. doi:10.1016/j.jneumeth.2007.03.024.