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

Non-parametric cluster-level test for spatio-temporal data.

This function provides a convenient wrapper for data organized in the form (observations x time x space) to use mne.stats.permutation_cluster_test(). See [1] for more information.

X: list of arrays

List of data arrays, shape (n_observations, n_times, n_vertices) in each group.

threshold : float | dict | None

If threshold is None, it will choose an F-threshold equivalent to p < 0.05 for the given number of observations (only valid when using an F-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

See permutation_cluster_test.

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

See permutation_cluster_test.

stat_fun : callable | None

Function called to calculate statistics, must accept 1d-arrays as arguments (default None uses mne.stats.f_oneway()).

connectivity : sparse matrix or None

Defines connectivity between features. The matrix is assumed to be symmetric and only the upper triangular half is used. Default is None, i.e, a regular lattice 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.

spatial_exclude : list of int or None

List of spatial indices to exclude from clustering.

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 F-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_times * n_vertices,)

Statistic (t by default) 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.


[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.