mne.SourceSpaces#

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

Represent a list of source space.

This class acts like a list of dictionaries containing the source space information, one entry in the list per source space type. See Notes for details.

Warning

This class should not be created or modified by the end user. Use mne.setup_source_space(), mne.setup_volume_source_space(), or mne.read_source_spaces() to create SourceSpaces.

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’.

See also

mne.setup_source_space: Setup a surface source space.
mne.setup_volume_source_space: Setup a volume source space.
mne.read_source_spaces: Read source spaces from a file.

Notes

Each element in SourceSpaces (e.g., src[0]) is a dictionary. For example, a surface source space will have len(src) == 2, one entry for each hemisphere. A volume source space will have len(src) == 1 if it uses a single monolithic grid, or len(src) == len(volume_label) when created with a list-of-atlas-labels. A mixed source space consists of both surface and volumetric source spaces in a single SourceSpaces object.

Each of those dictionaries can be accessed using standard Python dict access using the string keys listed below (e.g., src[0]['type'] == 'surf'). The relevant key/value pairs depend on the source space type:

Relevant to all source spaces

The following are always present:

idint
The FIF ID, either FIFF.FIFFV_MNE_SURF_LEFT_HEMI or FIFF.FIFFV_MNE_SURF_RIGHT_HEMI for surfaces, or FIFF.FIFFV_MNE_SURF_UNKNOWN for volume source spaces.

typestr
The type of source space, one of {'surf', 'vol', 'discrete'}.

npint
Number of vertices in the dense surface or complete volume.

coord_frameint
The coordinate frame, usually FIFF.FIFFV_COORD_MRI.

rrndarray, shape (np, 3)
The dense surface or complete volume vertex locations.

nnndarray, shape (np, 3)
The dense surface or complete volume normals.

nuseint
The number of points in the subsampled surface.

inusendarray, shape (np,)
An integer array defining whether each dense surface vertex is used (1) or unused (0).

vertnondarray, shape (n_src,)
The vertex numbers of the dense surface or complete volume that are used (i.e., np.where(src[0]['inuse'])[0]).

subject_his_idstr
The FreeSurfer subject name.

Surface source spaces

Surface source spaces created using mne.setup_source_space() can have the following additional entries (which will be missing, or have values of None or 0 for volumetric source spaces):

ntriint
Number of triangles in the dense surface triangulation.

trisndarray, shape (ntri, 3)
The dense surface triangulation.

nuse_triint
The number of triangles in the subsampled surface.

use_trisndarray, shape (nuse_tri, 3)
The subsampled surface triangulation.

distscipy.sparse.csr_matrix, shape (n_src, n_src) | None
The distances (euclidean for volume, along the cortical surface for surfaces) between source points.

dist_limitfloat
The maximum distance allowed for inclusion in nearest.

pinfolist of ndarray
For each vertex in the subsampled surface, the indices of the vertices in the dense surface that it represents (i.e., is closest to of all subsampled indices), e.g. for the left hemisphere (here constructed for sample with spacing='oct-6'), which vertices did we choose? Note the first is 14:
>>> src[0]['vertno']  
array([    14,     54,     59, ..., 155295, 155323, 155330])
And which dense surface verts did our vertno[0] (14 on dense) represent?
>>> src[0]['pinfo'][0]  
array([  6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,  18,
        19,  20,  21,  22,  23,  24,  25,  29,  30,  31,  39, 134, 135,
       136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 151, 152, 156,
       162, 163, 173, 174, 185, 448, 449, 450, 451, 452, 453, 454, 455,
       456, 462, 463, 464, 473, 474, 475, 485, 496, 497, 512, 864, 876,
       881, 889, 890, 904])
patch_indsndarray, shape (n_src_remaining,)
The patch indices that have been retained (if relevant, following forward computation. After just mne.setup_source_space(), this will be np.arange(src[0]['nuse']). After forward computation, some vertices can be excluded, in which case this tells you which patches (of the original np.arange(nuse)) are still in use. So if some vertices have been excluded, the line above for pinfo for completeness should be (noting that the first subsampled vertex ([0]) represents the following dense vertices):
>>> src[0]['pinfo'][src[0]['patch_inds'][0]]  
array([  6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,  18,
        19,  20,  21,  22,  23,  24,  25,  29,  30,  31,  39, 134, 135,
       136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 151, 152, 156,
       162, 163, 173, 174, 185, 448, 449, 450, 451, 452, 453, 454, 455,
       456, 462, 463, 464, 473, 474, 475, 485, 496, 497, 512, 864, 876,
       881, 889, 890, 904])
nearestndarray, shape (np,)
For each vertex on the dense surface, this gives the vertex index (in the dense surface) that each dense surface vertex is closest to of the vertices chosen for subsampling. This is essentially the reverse map off pinfo, e.g.:
>>> src[0]['nearest'].shape  
(115407,)
Based on pinfo above, this should be 14:
>>> src[0]['nearest'][6]  
14
nearest_distndarray, shape (np,)
The distances corresponding to nearest.

Volume source spaces

Volume source spaces created using mne.setup_volume_source_space() can have the following additional entries (which will be missing, or have values of None or 0 for surface source spaces):

mri_width, mri_height, mri_depthint
The MRI dimensions (in voxels).

neighbor_vertndarray
The 26-neighborhood information for each vertex.

interpolatorscipy.sparse.csr_matrix | None
The linear interpolator to go from the subsampled volume vertices to the high-resolution volume.

shapetuple of int
The shape of the subsampled grid.

mri_ras_tinstance of Transform
The transformation from MRI surface RAS (FIFF.FIFFV_COORD_MRI) to MRI scanner RAS (FIFF.FIFFV_MNE_COORD_RAS).

src_mri_tinstance of Transform
The transformation from subsampled source space voxel to MRI surface RAS.

vox_mri_tinstance of Transform
The transformation from the original MRI voxel (FIFF.FIFFV_MNE_COORD_MRI_VOXEL) space to MRI surface RAS.

mri_volume_namestr
The MRI volume name, e.g. 'subjects_dir/subject/mri/T1.mgz'.

seg_namestr
The MRI atlas segmentation name (e.g., 'Left-Cerebellum-Cortex' from the parameter volume_label).

Source spaces also have some attributes that are accessible via . access, like src.kind.

Attributes:

kindstr: The kind of source space, one of {'surface', 'volume', 'discrete', 'mixed'}.
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.
`__getitem__`(args, *kwargs)	Get an item.
`copy`()	Make a copy of the source spaces.
`export_volume`(fname[, include_surfaces, ...])	Export source spaces to nifti or mgz file.
`plot`([head, brain, skull, subjects_dir, ...])	Plot the source space.
`save`(fname[, overwrite, verbose])	Save the source spaces to a fif file.

__add__(other)[source]#: Combine source spaces.

__getitem__(*args, **kwargs)[source]#: Get an item.

copy()[source]#

Make a copy of the source spaces.

Returns:

srcinstance of SourceSpaces: The copied source spaces.

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:

fnamepath-like: 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.

Notes

This method requires nibabel.

Examples using export_volume:

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

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_dirpath-like | None: Path to SUBJECTS_DIR if it is not set in the environment.
transpath-like | '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.

Returns:

figinstance of Figure3D: The figure.

Examples using plot:

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

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

Save the source spaces to a fif file.

Parameters:

fnamepath-like: 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.

Examples using `mne.SourceSpaces`#

Setting the EEG reference

FreeSurfer MRI reconstruction

Source alignment and coordinate frames

Head model and forward computation

EEG forward operator with a template MRI

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs

The SourceEstimate data structure

Source reconstruction using an LCMV beamformer

Visualize source time courses (stcs)

Permutation t-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

Working with sEEG data

Working with ECoG data

Compare simulated and estimated source activity

Generate simulated raw data

Simulate raw data using subject anatomy

Generate simulated source data

Display sensitivity maps for EEG and MEG sensors

Generate a left cerebellum volume source space

Use source space morphing

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

Generate a functional label from source estimates

Extracting the time series of activations in a label

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

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

Morph surface source estimate

Morph volumetric source estimate

Visualize source leakage among labels using a circular graph

Plot point-spread functions (PSFs) for a volume

Reading an inverse operator

mne.SourceSpaces#

Examples using mne.SourceSpaces#

Examples using `mne.SourceSpaces`#