mne.viz.plot_ica_components

mne.viz.plot_ica_components(ica, picks=None, ch_type=None, res=64, layout=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=False, title=None, show=True, outlines='head', contours=6, image_interp='bilinear', head_pos=None)

Project unmixing matrix on interpolated sensor topogrpahy.

Parameters:

ica : instance of mne.preprocessing.ICA

The ICA solution.

picks : int | array-like | None

The indices of the sources to be plotted. If None all are plotted in batches of 20.

ch_type : ‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None

The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then channels are chosen in the order given above.

res : int

The resolution of the topomap image (n pixels along each side).

layout : None | Layout

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout is inferred from the data.

vmin : float | callable | None

The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). Defaults to None.

vmax : float | callable | None

The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If callable, the output equals vmax(data). Defaults to None.

cmap : matplotlib colormap

Colormap.

sensors : bool | str

Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True, a circle will be used (via .add_artist). Defaults to True.

colorbar : bool

Plot a colorbar.

title : str | None

Title to use.

show : bool

Show figure if True.

outlines : ‘head’ | ‘skirt’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask, and the ‘autoshrink’ (bool) field will trigger automated shrinking of the positions due to points outside the outline. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

contours : int | False | None

The number of contour lines to draw. If 0, no contours will be drawn.

image_interp : str

The image interpolation to be used. All matplotlib options are accepted.

head_pos : dict | None

If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’ (tuple) for what the center and scale of the head should be relative to the electrode locations.

Returns:

fig : instance of matplotlib.pyplot.Figure or list

The figure object(s).