mne.channels.DigMontage#

class mne.channels.DigMontage(*, dig=None, ch_names=None)[source]#

Montage for digitized electrode and headshape position data.

Warning

Montages are typically created using one of the helper functions in the See Also section below instead of instantiating this class directly.

Parameters:

diglist of dict: The object containing all the dig points.
ch_nameslist of str: The names of the EEG channels.

See also

Source alignment and coordinate frames

Notes

Since MNE uses the FIF data structure, it relies on the head coordinate frame. Any coordinate frame can be transformed to head if the fiducials (i.e. LPA, RPA and Nasion) are defined. One can use this function to estimate those fiducials and then use mne.channels.compute_native_head_t(montage) to get the head <-> MRI transform.

Examples using add_estimated_fiducials:

Locating intracranial electrode contacts

add_mni_fiducials(subjects_dir=None, verbose=None)[source]#

Add fiducials to a montage in MNI space.

Parameters:

subjects_dirpath-like | None: The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of DigMontage: The instance, modified in-place.

Notes

fsaverage is in MNI space and so its fiducials can be added to a montage in “mni_tal”. MNI is an ACPC-aligned coordinate system (the posterior commissure is the origin) so since BIDS requires channel locations for ECoG, sEEG and DBS to be in ACPC space, this function can be used to allow those coordinate to be transformed to “head” space (origin between LPA and RPA).

Examples using add_mni_fiducials:

Working with ECoG data

apply_trans(trans, verbose=None)[source]#

Apply a transformation matrix to the montage.

Parameters:

transinstance of mne.transforms.Transform: The transformation matrix to be applied.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Examples using apply_trans:

Locating intracranial electrode contacts

Working with sEEG data

How to convert 3D electrode positions to a 2D image

copy()[source]#

Copy the DigMontage object.

Returns:

diginstance of DigMontage: The copied DigMontage instance.

get_positions()[source]#

Get all channel and fiducial positions.

Returns:

positionsdict

A dictionary of the positions for channels (ch_pos), coordinate frame (coord_frame), nasion (nasion), left preauricular point (lpa), right preauricular point (rpa), Head Shape Polhemus (hsp), and Head Position Indicator(hpi). E.g.:

{
    'ch_pos': {'EEG061': [0, 0, 0]},
    'nasion': [0, 0, 1],
    'coord_frame': 'mni_tal',
    'lpa': [0, 1, 0],
    'rpa': [1, 0, 0],
    'hsp': None,
    'hpi': None
}

Examples using get_positions:

Working with ECoG data

plot(scale_factor=20, show_names=True, kind='topomap', show=True, sphere=None, verbose=None)[source]#

Plot a montage.

Parameters:

scale_factorfloat: Determines the size of the points.
show_namesbool | list: Whether to display all channel names. If a list, only the channel names in the list are shown. Defaults to True.
kindstr: Whether to plot the montage as ‘3d’ or ‘topomap’ (default).
showbool: Show figure if True.
spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’: The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figinstance of matplotlib.figure.Figure: The figure object.

Examples using plot:

Working with sensor locations

EEG source localization given electrode locations on an MRI

remove_fiducials(verbose=None)[source]#

Remove the fiducial points from a montage.

Parameters:

verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of DigMontage: The instance, modified in-place.

Notes

MNE will transform a montage to the internal “head” coordinate frame if the fiducials are present. Under most circumstances, this is ideal as it standardizes the coordinate frame for things like plotting. However, in some circumstances, such as saving a raw with intracranial data to BIDS format, the coordinate frame should not be changed by removing fiducials.

rename_channels(mapping, allow_duplicates=False)[source]#

Rename the channels.

Parameters:

mappingdict | callable(): A dictionary mapping the old channel to a new channel name e.g. {‘EEG061’ : ‘EEG161’}. Can also be a callable function that takes and returns a string.

Changed in version 0.10.0: Support for a callable function.
allow_duplicatesbool: If True (default False), allow duplicates, which will automatically be renamed with -N at the end.

New in version 0.22.0.

Returns:

instinstance of DigMontage: The instance. Operates in-place.

Examples using rename_channels:

EEG forward operator with a template MRI

save(fname, *, overwrite=False, verbose=None)[source]#

Save digitization points to FIF.

Parameters:

fnamepath-like: The filename to use. Should end in .fif or .fif.gz.
overwritebool: If True (default False), overwrite the destination file if it exists.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Examples using `mne.channels.DigMontage`#

Working with sensor locations

Importing data from fNIRS devices

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

EEG forward operator with a template MRI

EEG source localization given electrode locations on an MRI

Locating intracranial electrode contacts

Working with sEEG data

Working with ECoG data

Identify EEG Electrodes Bridged by too much Gel

Removing muscle ICA components

How to convert 3D electrode positions to a 2D image

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Receptive Field Estimation and Prediction

`__add__`(other)	Add two DigMontages.
`add_estimated_fiducials`(subject[, ...])	Estimate fiducials based on FreeSurfer `fsaverage` subject.
`add_mni_fiducials`([subjects_dir, verbose])	Add fiducials to a montage in MNI space.
`apply_trans`(trans[, verbose])	Apply a transformation matrix to the montage.
`copy`()	Copy the DigMontage object.
`get_positions`()	Get all channel and fiducial positions.
`plot`([scale_factor, show_names, kind, show, ...])	Plot a montage.
`remove_fiducials`([verbose])	Remove the fiducial points from a montage.
`rename_channels`(mapping[, allow_duplicates])	Rename the channels.
`save`(fname, *[, overwrite, verbose])	Save digitization points to FIF.

mne.channels.DigMontage#

Examples using mne.channels.DigMontage#

Examples using `mne.channels.DigMontage`#