Skip to main content

StatsAdjacency

Namespace: STATSLIB  ·  Library: Statistics Library

Python equivalent

mne.stats.combine_adjacency in MNE-Python.

#include <sts/sts_adjacency.h>

class STSLIB::StatsAdjacency

Adjacency matrix construction for spatial clustering.

Builds the sparse spatial and spatio-temporal neighbourhood graphs that define cluster support for permutation tests.

Static Methods​

fromChannelPositions(info, picks)​

Build a spatial adjacency matrix from channel positions in FiffInfo.

Uses a distance threshold of 3x the median nearest-neighbor distance.

Parameters:

  • info : const FiffInfo & FiffInfo with channel positions.

  • picks : const QStringList & Optional list of channel names to include. If empty, all channels are used.

Returns:

  • Eigen::SparseMatrix< int > — Sparse adjacency matrix (symmetric, nChannels x nChannels).

fromSourceSpace(tris, nVertices)​

Build a spatial adjacency matrix from a triangulated source space.

Parameters:

  • tris : const Eigen::MatrixX3i & Triangle definitions (nTris x 3, vertex indices).

  • nVertices : int Total number of vertices.

Returns:

  • Eigen::SparseMatrix< int > — Sparse adjacency matrix (symmetric, nVertices x nVertices).

fromSourceSpaceTemporal(tris, nVertices, nTimes)​

Build a spatio-temporal adjacency matrix from a triangulated source space.

Constructs a (nVerticesnTimes) x (nVerticesnTimes) adjacency matrix where spatial neighbors come from the triangle mesh and temporal neighbors connect each vertex to itself at t-1 and t+1. The linear index is vertex * nTimes + time.

Parameters:

  • tris : const Eigen::MatrixX3i & Triangle definitions (nTris x 3, vertex indices).

  • nVertices : int Total number of vertices.

  • nTimes : int Number of time points.

Returns:

  • Eigen::SparseMatrix< int > — Sparse adjacency matrix (symmetric, nVerticesnTimes x nVerticesnTimes).

Authors of this file​