mne.stats.summarize_clusters_stc¶

mne.stats.summarize_clusters_stc(clu, p_thresh=0.05, tstep=0.001, tmin=0, subject='fsaverage', vertices=None)[source]¶

Assemble summary SourceEstimate from spatiotemporal cluster results.

This helps visualizing results from spatio-temporal-clustering permutation tests.

Parameters:

Parameters:	clu : tuple the output from clustering permutation tests. p_thresh : float The significance threshold for inclusion of clusters. tstep : float The temporal difference between two time samples. tmin : float \| int The time of the first sample. subject : str The name of the subject. vertices : list of arrays \| None The vertex numbers associated with the source space locations. Defaults to None. If None, equals `[np.arange(10242), np.arange(10242)]`.
Returns:	out : instance of SourceEstimate A summary of the clusters. The first time point in this SourceEstimate object is the summation of all the clusters. Subsequent time points contain each individual cluster. The magnitude of the activity corresponds to the length the cluster spans in time (in samples).

clu : tuple: the output from clustering permutation tests.
p_thresh : float: The significance threshold for inclusion of clusters.
tstep : float: The temporal difference between two time samples.
tmin : float | int: The time of the first sample.
subject : str: The name of the subject.
vertices : list of arrays | None: The vertex numbers associated with the source space locations. Defaults to None. If None, equals `[np.arange(10242), np.arange(10242)]`.

Returns:

out : instance of SourceEstimate: A summary of the clusters. The first time point in this SourceEstimate object is the summation of all the clusters. Subsequent time points contain each individual cluster. The magnitude of the activity corresponds to the length the cluster spans in time (in samples).

Examples using `mne.stats.summarize_clusters_stc`¶