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.

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.

Examples using mne.channels.find_ch_adjacency#

Visualising statistical significance thresholds on EEG data

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

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

Spatiotemporal permutation F-test on full sensor data