sts_adjacency.h File Reference

Construction of the sensor- and source-space neighbourhood graphs that define the cluster support for permutation testing. More...

#include "sts_global.h"
#include <fiff/fiff_info.h>
#include <QStringList>
#include <Eigen/Core>
#include <Eigen/SparseCore>

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Classes
class	STSLIB::StatsAdjacency
	Builds the sparse spatial and spatio-temporal neighbourhood graphs that define cluster support for permutation tests. More...

Namespaces
namespace	STSLIB
	Statistical testing (t-tests, F-tests, cluster permutation, multiple comparison correction).

Detailed Description

Construction of the sensor- and source-space neighbourhood graphs that define the cluster support for permutation testing.

SPDX-License-Identifier: BSD-3-Clause Copyright (c) 2026 MNE-CPP Authors

Author

Christoph Dinh chris.nosp@m.toph.nosp@m..dinh.nosp@m.@mne.nosp@m.-cpp..nosp@m.org

Since

2.2.0

Date

April 2026

Cluster-based inference (STSLIB::StatsCluster) reduces the multiple-comparison problem by grouping supra-threshold samples into spatially - and optionally temporally - connected clusters before computing a max-statistic null. Doing this rigorously requires an explicit adjacency graph whose edges encode which (channel, time) or (vertex, time) pairs are allowed to merge.

STSLIB::StatsAdjacency provides three constructors that cover the standard M/EEG analysis cases: a sensor graph built from the 3D channel positions in a FIFFLIB::FiffInfo using a heuristic of three times the median nearest-neighbour distance, a cortical graph built from the triangulation of a source space, and a spatio-temporal extension of the cortical graph that links each vertex to itself at the previous and next time sample (linear index vertex*nTimes+time). The output is always a symmetric sparse integer matrix consumed unchanged by STSLIB::StatsCluster.

Reference: Maris & Oostenveld (2007), J. Neurosci. Methods 164(1).

Definition in file sts_adjacency.h.