mne.stats.spatio_temporal_cluster_1samp_test¶

mne.stats.
spatio_temporal_cluster_1samp_test
(X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, connectivity=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, verbose=None)[source]¶ Nonparametric clusterlevel paired ttest for spatiotemporal data.
This function provides a convenient wrapper for data organized in the form (observations x time x space) to use
mne.stats.permutation_cluster_1samp_test()
, which contains more complete documentation. Parameters
 X
array
, shape (n_observations, n_times, n_vertices) Array data of the difference between two conditions.
 threshold
float
dict
None
If threshold is None, it will choose a tthreshold equivalent to p < 0.05 for the given number of observations (only valid when using an tstatistic). If a dict is used, then thresholdfree 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 number of permutations to compute. Can be “all” to perform an exact test.
 tail1 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
scipy.sparse.spmatrix
orNone
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.
 n_jobs
int
Number of permutations to run in parallel (requires joblib package).
 seed
int
 instance ofRandomState
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
ofint
orNone
List of spatial indices to exclude from clustering.
 step_down_p
float
To perform a stepdowninjumps test, pass a pvalue for clusters to exclude from each successive iteration. Default is zero, perform no stepdown 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 tvalues) 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_disjointbool
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.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 X
 Returns
References
 1
Maris/Oostenveld, “Nonparametric statistical testing of EEG and MEGdata” Journal of Neuroscience Methods, Vol. 164, No. 1., pp. 177190. doi:10.1016/j.jneumeth.2007.03.024
 2
Smith/Nichols (2009), “Thresholdfree cluster enhancement: Addressing problems of smoothing, threshold dependence, and localisation in cluster inference”, NeuroImage 44 (2009) 8398.