Parameters: |
- icas : list of mne.preprocessing.ICA
A list of fitted ICA objects.
- template : tuple | np.ndarray, shape (n_components,)
Either a tuple with two elements (int, int) representing the list
indices of the set from which the template should be chosen, and the
template. E.g., if template=(1, 0), the first IC of the 2nd ICA object
is used.
Or a numpy array whose size corresponds to each IC map from the
supplied maps, in which case this map is chosen as the template.
- threshold : “auto” | list of float | float
Correlation threshold for identifying ICs
If “auto”, search for the best map by trying all correlations between
0.6 and 0.95. In the original proposal, lower values are considered,
but this is not yet implemented.
If list of floats, search for the best map in the specified range of
correlation strengths. As correlation values, must be between 0 and 1
If float > 0, select ICs correlating better than this.
If float > 1, use find_outliers to identify ICs within subjects (not in
original Corrmap)
Defaults to “auto”.
- label : None | str
If not None, categorised ICs are stored in a dictionary labels_
under the given name. Preexisting entries will be appended to
(excluding repeats), not overwritten. If None, a dry run is performed
and the supplied ICs are not changed.
- ch_type : ‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’
The channel type to plot. Defaults to ‘eeg’.
- plot : bool
Should constructed template and selected maps be plotted? Defaults
to True.
- show : bool
Show figures if True.
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
- outlines : ‘head’ | dict | None
The outlines to be drawn. If ‘head’, a head scheme will be drawn. If
dict, each key refers to a tuple of x and y positions. The values in
‘mask_pos’ will serve as image mask. If None, nothing will be drawn.
Defaults to ‘head’. If dict, the ‘autoshrink’ (bool) field will
trigger automated shrinking of the positions due to points outside the
outline. Moreover, 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).
- layout : None | Layout | list of Layout
Layout instance specifying sensor positions (does not need to be
specified for Neuromag data). Or a list of Layout if projections
are from different sensor types.
- 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.
- contours : int | array of float
The number of contour lines to draw. If 0, no contours will be drawn.
When an integer, matplotlib ticker locator is used to find suitable
values for the contour thresholds (may sometimes be inaccurate, use
array for accuracy). If an array, the values represent the levels for
the contours. Defaults to 6.
- cmap : None | matplotlib colormap
Colormap for the plot. If None , defaults to ‘Reds_r’ for norm data,
otherwise to ‘RdBu_r’.
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