mne_connectivity.viz.plot_sensors_connectivity

mne_connectivity.viz.plot_sensors_connectivity(info, con, picks=None, *, cbar_label='Connectivity', n_con=20, cmap='RdBu', min_distance=0.05)

Visualize the sensor connectivity in 3D.

Parameters:

infodict | None

The measurement info.

conarray, shape (n_channels, n_channels) | Connectivity

The computed connectivity measure(s).

picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values 'all' to pick all channels, or 'data' to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided. Indices of selected channels.

cbar_labelstr

Label for the colorbar.

n_conint

Number of strongest connections shown. By default 20.

cmapstr | instance of matplotlib.colors.Colormap

Colormap for coloring connections by strength. If str, must be a valid Matplotlib colormap (i.e. a valid key of matplotlib.colormaps). Default is "RdBu".

min_distancefloat

The minimum distance required between two sensors to plot a connection between them, in meters. Default is 0.05 (i.e. 5 cm).

Added in version 0.8.

Returns:

figinstance of Renderer

The 3D figure.

Examples using mne_connectivity.viz.plot_sensors_connectivity

Comparing spectral connectivity computed over time or over trials

Comparing spectral connectivity computed over time or over trials

Compute all-to-all connectivity in sensor space

Compute all-to-all connectivity in sensor space