mne.channels.DigMontage

class mne.channels.DigMontage(hsp, hpi, elp, point_names, nasion=None, lpa=None, rpa=None, dev_head_t=None, dig_ch_pos=None)

Montage for Digitized data

Montages are typically loaded from a file using read_dig_montage. Only use this class directly if you’re constructing a new montage.

Parameters:

hsp : array, shape (n_points, 3)

The positions of the headshape points in 3d. These points are in the native digitizer space.

hpi : array, shape (n_hpi, 3)

The positions of the head-position indicator coils in 3d. These points are in the MEG device space.

elp : array, shape (n_hpi, 3)

The positions of the head-position indicator coils in 3d. This is typically in the native digitizer space.

point_names : list, shape (n_elp)

The names of the digitized points for hpi and elp.

nasion : array, shape (1, 3)

The position of the nasion fidicual point.

lpa : array, shape (1, 3)

The position of the left periauricular fidicual point.

rpa : array, shape (1, 3)

The position of the right periauricular fidicual point.

dev_head_t : array, shape (4, 4)

A Device-to-Head transformation matrix.

dig_ch_pos : dict

Dictionary of channel positions.

New in version 0.12.

Notes

New in version 0.9.0.

Methods

__hash__() <==> hash(x)
plot([scale_factor, show_names]) Plot EEG sensor montage
__hash__() <==> hash(x)
plot(scale_factor=1.5, show_names=False)

Plot EEG sensor montage

Parameters:

scale_factor : float

Determines the size of the points. Defaults to 1.5

show_names : bool

Whether to show the channel names. Defaults to False

Returns:

fig : Instance of matplotlib.figure.Figure

The figure object.