mne.channels.find_ch_adjacency#

mne.channels.find_ch_adjacency(info, ch_type)[source]#

Find the adjacency matrix for the given channels.

This function tries to infer the appropriate adjacency matrix template for the given channels. If a template is not found, the adjacency matrix is computed using Delaunay triangulation based on 2D sensor locations.

Parameters:

infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

ch_typestr | None

The channel type for computing the adjacency matrix. Currently supports 'mag', 'grad', 'eeg' and None. If None, the info must contain only one channel type.

Returns:

ch_adjacencyscipy.sparse.csr_matrix, shape (n_channels, n_channels)

The adjacency matrix.

ch_nameslist

The list of channel names present in adjacency matrix.

See also

mne.viz.plot_ch_adjacency

mne.stats.combine_adjacency

get_builtin_ch_adjacencies

read_ch_adjacency

Notes

New in version 0.15.

Automatic detection of an appropriate adjacency matrix template only works for MEG data at the moment. This means that the adjacency matrix is always computed for EEG data and never loaded from a template file. If you want to load a template for a given montage use read_ch_adjacency() directly.

Note that depending on your use case, you may need to additionally use mne.stats.combine_adjacency() to prepare a final “adjacency” to pass to the eventual function.

Examples using mne.channels.find_ch_adjacency#

Statistical inference

Statistical inference

Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power

Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data