mne.SourceSpaces

class mne.SourceSpaces(source_spaces, info=None)[source]

Represent a list of source space.

Currently implemented as a list of dictionaries containing the source space information

Parameters
source_spaceslist

A list of dictionaries containing the source space information.

infodict

Dictionary with information about the creation of the source space file. Has keys ‘working_dir’ and ‘command_line’.

Attributes
infodict

Dictionary with information about the creation of the source space file. Has keys ‘working_dir’ and ‘command_line’.

Methods

__add__(other)

Combine source spaces.

__contains__(key, /)

Return key in self.

__getitem__(*args, **kwargs)

Get an item.

__iter__(/)

Implement iter(self).

__len__(/)

Return len(self).

__mul__(value, /)

Return self*value.

append(object, /)

Append object to the end of the list.

clear(/)

Remove all items from list.

copy()

Make a copy of the source spaces.

count(value, /)

Return number of occurrences of value.

export_volume(fname[, include_surfaces, ...])

Export source spaces to nifti or mgz file.

extend(iterable, /)

Extend list by appending elements from the iterable.

index(value[, start, stop])

Return first index of value.

insert(index, object, /)

Insert object before index.

plot([head, brain, skull, subjects_dir, ...])

Plot the source space.

pop([index])

Remove and return item at index (default last).

remove(value, /)

Remove first occurrence of value.

reverse(/)

Reverse IN PLACE.

save(fname[, overwrite, verbose])

Save the source spaces to a fif file.

sort(*[, key, reverse])

Sort the list in ascending order and return None.

__add__(other)[source]

Combine source spaces.

__contains__(key, /)

Return key in self.

__getitem__(*args, **kwargs)[source]

Get an item.

__hash__ = None
__iter__(/)

Implement iter(self).

__len__(/)

Return len(self).

__mul__(value, /)

Return self*value.

append(object, /)

Append object to the end of the list.

clear(/)

Remove all items from list.

copy()[source]

Make a copy of the source spaces.

Returns
srcinstance of SourceSpaces

The copied source spaces.

count(value, /)

Return number of occurrences of value.

export_volume(fname, include_surfaces=True, include_discrete=True, dest='mri', trans=None, mri_resolution=False, use_lut=True, overwrite=False, verbose=None)[source]

Export source spaces to nifti or mgz file.

Parameters
fnamestr

Name of nifti or mgz file to write.

include_surfacesbool

If True, include surface source spaces.

include_discretebool

If True, include discrete source spaces.

dest‘mri’ | ‘surf’

If ‘mri’ the volume is defined in the coordinate system of the original T1 image. If ‘surf’ the coordinate system of the FreeSurfer surface is used (Surface RAS).

transdict, str, or None

Either a transformation filename (usually made using mne_analyze) or an info dict (usually opened using read_trans()). If string, an ending of .fif or .fif.gz will be assumed to be in FIF format, any other ending will be assumed to be a text file with a 4x4 transformation matrix (like the --trans MNE-C option. Must be provided if source spaces are in head coordinates and include_surfaces and mri_resolution are True.

mri_resolutionbool | str

If True, the image is saved in MRI resolution (e.g. 256 x 256 x 256), and each source region (surface or segmentation volume) filled in completely. If “sparse”, only a single voxel in the high-resolution MRI is filled in for each source point.

Changed in version 0.21.0: Support for “sparse” was added.

use_lutbool

If True, assigns a numeric value to each source space that corresponds to a color on the freesurfer lookup table.

overwritebool

If True (default False), overwrite the destination file if it exists.

New in version 0.19.

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. Defaults to self.verbose.

Notes

This method requires nibabel.

Examples using export_volume:

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
extend(iterable, /)

Extend list by appending elements from the iterable.

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

insert(index, object, /)

Insert object before index.

plot(head=False, brain=None, skull=None, subjects_dir=None, trans=None, verbose=None)[source]

Plot the source space.

Parameters
headbool

If True, show head surface.

brainbool | str

If True, show the brain surfaces. Can also be a str for surface type (e.g., ‘pial’, same as True). Default is None, which means ‘white’ for surface source spaces and False otherwise.

skullbool | str | list of str | list of dict | None

Whether to plot skull surface. If string, common choices would be ‘inner_skull’, or ‘outer_skull’. Can also be a list to plot multiple skull surfaces. If a list of dicts, each dict must contain the complete surface info (such as you get from mne.make_bem_model()). True is an alias of ‘outer_skull’. The subjects bem and bem/flash folders are searched for the ‘surf’ files. Defaults to None, which is False for surface source spaces, and True otherwise.

subjects_dirstr | None

Path to SUBJECTS_DIR if it is not set in the environment.

transstr | ‘auto’ | dict | None

The full path to the head<->MRI transform *-trans.fif file produced during coregistration. If trans is None, an identity matrix is assumed. This is only needed when the source space is in head coordinates.

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. Defaults to self.verbose.

Returns
figinstance of PyVista renderer

The figure.

Examples using plot:

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
pop(index=- 1, /)

Remove and return item at index (default last).

Raises IndexError if list is empty or index is out of range.

remove(value, /)

Remove first occurrence of value.

Raises ValueError if the value is not present.

reverse(/)

Reverse IN PLACE.

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

Save the source spaces to a fif file.

Parameters
fnamestr

File to write.

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. Defaults to self.verbose.

sort(*, key=None, reverse=False)

Sort the list in ascending order and return None.

The sort is in-place (i.e. the list itself is modified) and stable (i.e. the order of two equal elements is maintained).

If a key function is given, apply it once to each list item and sort them, ascending or descending, according to their function values.

The reverse flag can be set to sort in descending order.

Examples using mne.SourceSpaces

Setting the EEG reference

Setting the EEG reference

Setting the EEG reference
Source alignment and coordinate frames

Source alignment and coordinate frames

Source alignment and coordinate frames
Head model and forward computation

Head model and forward computation

Head model and forward computation
EEG forward operator with a template MRI

EEG forward operator with a template MRI

EEG forward operator with a template MRI
How MNE uses FreeSurfer's outputs

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs
The :class:`SourceEstimate <mne.SourceEstimate>` data structure

The SourceEstimate data structure

The :class:`SourceEstimate ` data structure
Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering
2 samples permutation test on source data with spatio-temporal clustering

2 samples permutation test on source data with spatio-temporal clustering

2 samples permutation test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering
Working with sEEG data

Working with sEEG data

Working with sEEG data
Working with ECoG data

Working with ECoG data

Working with ECoG data
Generate simulated raw data

Generate simulated raw data

Generate simulated raw data
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Generate simulated source data

Generate simulated source data

Generate simulated source data
Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors
Generate a left cerebellum volume source space

Generate a left cerebellum volume source space

Generate a left cerebellum volume source space
Use source space morphing

Use source space morphing

Use source space morphing
Compute MNE-dSPM inverse solution on evoked data in volume source space

Compute MNE-dSPM inverse solution on evoked data in volume source space

Compute MNE-dSPM inverse solution on evoked data in volume source space
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Extracting the time series of activations in a label

Extracting the time series of activations in a label

Extracting the time series of activations in a label
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
Morph surface source estimate

Morph surface source estimate

Morph surface source estimate
Morph volumetric source estimate

Morph volumetric source estimate

Morph volumetric source estimate
Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph
Reading an inverse operator

Reading an inverse operator

Reading an inverse operator