Container for surface source estimates.
array
of shape (n_dipoles, n_times) | tuple
, shape (2,)The data in source space. When it is a single array, the left hemisphere is stored in data[:len(vertices[0])] and the right hemisphere is stored in data[-len(vertices[1]):]. When data is a tuple, it contains two arrays:
“kernel” shape (n_vertices, n_sensors) and
“sens_data” shape (n_sensors, n_times).
In this case, the source space data corresponds to
np.dot(kernel, sens_data)
.
list
of array
, shape (2,)Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere.
Time point of the first sample in data.
Time step between successive samples in data.
str
The FreeSurfer subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors.
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.
See also
VectorSourceEstimate
A container for vector source estimates.
VolSourceEstimate
A container for volume source estimates.
MixedSourceEstimate
A container for mixed surface + volume source estimates.
Methods
|
Add source estimates. |
|
Divide source estimates. |
|
Multiply source estimates. |
|
Negate the source estimate. |
|
Subtract source estimates. |
|
Baseline correct source estimate data. |
|
Return a source estimate object with data summarized over time bins. |
|
Compute the center of mass of activity. |
|
Return copy of source estimate instance. |
|
Restrict SourceEstimate to a time interval. |
|
Decimate the time-series data. |
|
Compute time-varying SNR in the source space. |
|
Expand SourceEstimate to include more vertices. |
|
Extract label time courses for lists of labels. |
|
Get location and latency of peak amplitude. |
|
Get a source estimate object restricted to a label. |
|
Make a summary stc file with mean over time points. |
|
Plot SourceEstimate. |
|
Resample data. |
|
Save the source estimates to a file. |
|
Shift time scale in epoched or evoked data. |
|
Take the square root. |
|
Make a summary stc file with sum over time points. |
|
Convert time to indices. |
|
Export data in tabular structure as a pandas DataFrame. |
|
Get a source estimate from morphed source to the original subject. |
|
Apply linear transform. |
|
Get data after a linear (time) transform has been applied. |
Baseline correct source estimate data.
None
| tuple
of length 2The time interval to consider as “baseline” when applying baseline
correction. If None
, do not apply baseline correction.
If a tuple (a, b)
, the interval is between a
and b
(in seconds), including the endpoints.
If a
is None
, the beginning of the data is used; and if b
is None
, it is set to the end of the interval.
If (None, None)
, the entire time interval is used.
Note
The baseline (a, b)
includes both endpoints, i.e. all
timepoints t
such that a <= t <= b
.
Correction is applied to each source individually in the following way:
Calculate the mean signal of the baseline period.
Subtract this mean from the entire source estimate data.
Note
Baseline correction is appropriate when signal and noise are approximately additive, and the noise level can be estimated from the baseline interval. This can be the case for non-normalized source activities (e.g. signed and unsigned MNE), but it is not the case for normalized estimates (e.g. signal-to-noise ratios, dSPM, sLORETA).
Defaults to (None, 0)
, i.e. beginning of the the data until
time point zero.
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.
SourceEstimate
The baseline-corrected source estimate object.
Notes
Baseline correction can be done multiple times.
Return a source estimate object with data summarized over time bins.
Time bins of width
seconds. This method is intended for
visualization only. No filter is applied to the data before binning,
making the method inappropriate as a tool for downsampling data.
Width of the individual bins in seconds.
None
Time point where the first bin starts. The default is the first time point of the stc.
None
Last possible time point contained in a bin (if the last bin would be shorter than width it is dropped). The default is the last time point of the stc.
callable()
Function that is applied to summarize the data. Needs to accept a
numpy.array as first input and an axis
keyword argument.
SourceEstimate
| VectorSourceEstimate
The binned source estimate.
Compute the center of mass of activity.
This function computes the spatial center of mass on the surface as well as the temporal center of mass as in [1].
Note
All activity must occur in a single hemisphere, otherwise
an error is raised. The “mass” of each point in space for
computing the spatial center of mass is computed by summing
across time, and vice-versa for each point in time in
computing the temporal center of mass. This is useful for
quantifying spatio-temporal cluster locations, especially
when combined with mne.vertex_to_mni()
.
str
| None
The subject the stc is defined for.
int
, or None
Calculate the center of mass for the left (0) or right (1) hemisphere. If None, one of the hemispheres must be all zeroes, and the center of mass will be calculated for the other hemisphere (useful for getting COM for clusters).
array
of int
| instance of SourceSpaces
If True, returned vertex will be one from stc. Otherwise, it could be any vertex from surf. If an array of int, the returned vertex will come from that array. If instance of SourceSpaces (as of 0.13), the returned vertex will be from the given source space. For most accuruate estimates, do not restrict vertices.
None
The path to the directory containing the FreeSurfer subjects
reconstructions. If None
, defaults to the SUBJECTS_DIR
environment
variable.
str
The surface to use for Euclidean distance center of mass finding. The default here is “sphere”, which finds the center of mass on the spherical surface to help avoid potential issues with cortical folding.
int
Vertex of the spatial center of mass for the inferred hemisphere, with each vertex weighted by the sum of the stc across time. For a boolean stc, then, this would be weighted purely by the duration each vertex was active.
int
Hemisphere the vertex was taken from.
float
Time of the temporal center of mass (weighted by the sum across source vertices).
References
Examples using center_of_mass
:
Extracting time course from source_estimate object
Return copy of source estimate instance.
SourceEstimate
A copy of the source estimate.
Examples using copy
:
Compute source power spectral density (PSD) of VectorView and OPM data
Generate a functional label from source estimates
Restrict SourceEstimate to a time interval.
float
| None
The first time point in seconds. If None the first present is used.
float
| None
The last time point in seconds. If None the last present is used.
If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).
New in version 0.19.
SourceEstimate
The cropped source estimate.
Examples using crop
:
Permutation t-test on source data with spatio-temporal clustering
Numpy array of source estimate data.
Decimate the time-series data.
int
Factor by which to subsample the data.
Warning
Low-pass filtering is not performed, this simply selects
every Nth sample (where N is the value passed to
decim
), i.e., it compresses the signal (see Notes).
If the data are not properly filtered, aliasing artifacts
may occur.
int
Apply an offset to where the decimation starts relative to the sample corresponding to t=0. The offset is in samples at the current sampling rate.
New in version 0.12.
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.
The decimated object.
See also
Notes
For historical reasons, decim
/ “decimation” refers to simply subselecting
samples from a given signal. This contrasts with the broader signal processing
literature, where decimation is defined as (quoting
[2], p. 172; which cites
[3]):
“… a general system for downsampling by a factor of M is the one shown in Figure 4.23. Such a system is called a decimator, and downsampling by lowpass filtering followed by compression [i.e, subselecting samples] has been termed decimation (Crochiere and Rabiner, 1983).”
Hence “decimation” in MNE is what is considered “compression” in the signal processing community.
Decimation can be done multiple times. For example,
inst.decimate(2).decimate(2)
will be the same as
inst.decimate(4)
.
If decim
is 1, this method does not copy the underlying data.
New in version 0.10.0.
References
Compute time-varying SNR in the source space.
This function should only be used with source estimates with units nanoAmperes (i.e., MNE-like solutions, not dSPM or sLORETA). See also [4].
Warning
This function currently only works properly for fixed orientation.
mne.Info
The mne.Info
object with information about the sensors and methods of measurement.
Forward
The forward solution used to create the source estimate.
Covariance
The noise covariance used to estimate the resting cortical activations. Should be an evoked covariance, not empty room.
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.
SourceEstimate
The source estimate with the SNR computed.
Notes
We define the SNR in decibels for each source location at each time point as:
where \(\\b_k\) is the signal on sensor \(k\) provided by the forward model for a source with unit amplitude, \(a\) is the source amplitude, \(N\) is the number of sensors, and \(s_k^2\) is the noise variance on sensor \(k\).
References
Examples using estimate_snr
:
Expand SourceEstimate to include more vertices.
This will add rows to stc.data (zero-filled) and modify stc.vertices to include all vertices in stc.vertices and the input vertices.
SourceEstimate
| VectorSourceEstimate
The modified stc (note: method operates inplace).
Extract label time courses for lists of labels.
This function will extract one time course for each label. The way the time courses are extracted depends on the mode parameter.
Label
| BiHemiLabel
| list
| tuple
| str
If using a surface or mixed source space, this should be the
Label
’s for which to extract the time course.
If working with whole-brain volume source estimates, this must be one of:
a string path to a FreeSurfer atlas for the subject (e.g., their ‘aparc.a2009s+aseg.mgz’) to extract time courses for all volumes in the atlas
a two-element list or tuple, the first element being a path to an atlas,
and the second being a list or dict of volume_labels
to extract
(see mne.setup_volume_source_space()
for details).
Changed in version 0.21.0: Support for volume source estimates.
SourceSpaces
The source spaces for the source time courses.
str
Extraction mode, see Notes.
str
False
(default) will emit an error if there are labels that have no
vertices in the source estimate. True
and 'ignore'
will return
all-zero time courses for labels that do not have any vertices in the
source estimate, and True will emit a warning while and “ignore” will
just log a message.
Changed in version 0.21.0: Support for “ignore”.
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.
See also
extract_label_time_course
Extract time courses for multiple STCs.
Notes
Valid values for mode
are:
'max'
Maximum value across vertices at each time point within each label.
'mean'
Average across vertices at each time point within each label. Ignores orientation of sources for standard source estimates, which varies across the cortical surface, which can lead to cancellation. Vector source estimates are always in XYZ / RAS orientation, and are thus already geometrically aligned.
'mean_flip'
Finds the dominant direction of source space normal vector orientations within each label, applies a sign-flip to time series at vertices whose orientation is more than 180° different from the dominant direction, and then averages across vertices at each time point within each label.
'pca_flip'
Applies singular value decomposition to the time courses within each label,
and uses the first right-singular vector as the representative label time
course. This signal is scaled so that its power matches the average
(per-vertex) power within the label, and sign-flipped by multiplying by
np.sign(u @ flip)
, where u
is the first left-singular vector and
flip
is the same sign-flip vector used when mode='mean_flip'
. This
sign-flip ensures that extracting time courses from the same label in
similar STCs does not result in 180° direction/phase changes.
'auto'
(default)Uses 'mean_flip'
when a standard source estimate is applied, and
'mean'
when a vector source estimate is supplied.
New in version 0.21: Support for 'auto'
, vector, and volume source estimates.
The only modes that work for vector and volume source estimates are 'mean'
,
'max'
, and 'auto'
.
Examples using extract_label_time_course
:
Generate a functional label from source estimates
Extracting the time series of activations in a label
Get location and latency of peak amplitude.
None
}The hemi to be considered. If None, the entire source space is considered.
float
| None
The minimum point in time to be considered for peak getting.
float
| None
The maximum point in time to be considered for peak getting.
How to deal with the sign of the data. If ‘pos’ only positive values will be considered. If ‘neg’ only negative values will be considered. If ‘abs’ absolute values will be considered. Defaults to ‘abs’.
Whether to return the vertex index (True) instead of of its ID (False, default).
Whether to return the time index (True) instead of the latency (False, default).
Examples using get_peak
:
The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Get a source estimate object restricted to a label.
SourceEstimate contains the time course of activation of all sources inside the label.
Label
| BiHemiLabel
The label (as created for example by mne.read_label). If the label does not match any sources in the SourceEstimate, a ValueError is raised.
SourceEstimate
| VectorSourceEstimate
The source estimate restricted to the given label.
Examples using in_label
:
Compute MNE-dSPM inverse solution on single epochs
Extracting time course from source_estimate object
Generate a functional label from source estimates
Extracting the time series of activations in a label
Left hemisphere data.
Left hemisphere vertno.
Make a summary stc file with mean over time points.
SourceEstimate
| VectorSourceEstimate
The modified stc.
Plot SourceEstimate.
str
| None
The FreeSurfer subject name.
If None
, stc.subject
will be used.
str
The type of surface (inflated, white etc.).
str
Hemisphere id (ie ‘lh’, ‘rh’, ‘both’, or ‘split’). In the case of ‘both’, both hemispheres are shown in the same window. In the case of ‘split’ hemispheres are displayed side-by-side in different viewing panes.
str
| np.ndarray
of float
, shape(n_colors, 3 | 4)Name of colormap to use or a custom look up table. If array, must be (n x 3) or (n x 4) array for with RGB or RGBA values between 0 and 255. The default (‘auto’) uses ‘hot’ for one-sided data and ‘mne’ for two-sided data.
str
| callable()
| None
Format of the time label (a format string, a function that maps
floating point time values to strings, or None for no label). The
default is 'auto'
, which will use time=%0.2f ms
if there
is more than one time point.
int
The amount of smoothing.
None
If True: use a linear transparency between fmin and fmid and make values below fmin fully transparent (symmetrically for divergent colormaps). None will choose automatically based on colormap type.
float
Alpha value to apply globally to the overlay. Has no effect with mpl backend.
str
Display time viewer GUI. Can also be ‘auto’, which will mean True for the PyVista backend and False otherwise.
Changed in version 0.20.0: “auto” mode added.
None
The path to the directory containing the FreeSurfer subjects
reconstructions. If None
, defaults to the SUBJECTS_DIR
environment
variable.
Figure3D
| instance of matplotlib.figure.Figure
| list
| int
| None
If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate length. If int is provided it will be used to identify the PyVista figure by it’s id or create a new figure with the given id. If an instance of matplotlib figure, mpl backend is used for plotting.
str
| list
View to use. Using multiple views (list) is not supported for mpl
backend. See Brain.show_view
for
valid string options.
When plotting a standard SourceEstimate (not volume, mixed, or vector)
and using the PyVista backend, views='flat'
is also supported to
plot cortex as a flatmap.
Using multiple views (list) is not supported by the matplotlib backend.
Changed in version 0.21.0: Support for flatmaps.
If True, display colorbar on scene.
str
| dict
Colorbar properties specification. If ‘auto’, set clim automatically based on data percentiles. If dict, should contain:
kind
‘value’ | ‘percent’Flag to specify type of limits.
lims
list | np.ndarray | tuple of float, 3 elementsLower, middle, and upper bounds for colormap.
pos_lims
list | np.ndarray | tuple of float, 3 elementsLower, middle, and upper bound for colormap. Positive values will be mirrored directly across zero during colormap construction to obtain negative control points.
Note
Only one of lims
or pos_lims
should be provided.
Only sequential colormaps should be used with lims
, and
only divergent colormaps should be used with pos_lims
.
str
or tuple
Specifies how binarized curvature values are rendered. Either the name of a preset Brain cortex colorscheme (one of ‘classic’, ‘bone’, ‘low_contrast’, or ‘high_contrast’), or the name of a colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. Has no effect with mpl backend.
float
or tuple
of float
The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. Has no effect with mpl backend.
Color of the background of the display window.
None
Color of the foreground of the display window. Has no effect with mpl backend. None will choose white or black based on the background color.
float
| None
The time to display on the plot initially. None
to display the
first time sample (default).
Whether time is represented in seconds (“s”, default) or milliseconds (“ms”).
Which backend to use. If 'auto'
(default), tries to plot with
pyvistaqt, but resorts to matplotlib if no 3d backend is available.
New in version 0.15.0.
str
Only affects the matplotlib backend.
The spacing to use for the source space. Can be 'ico#'
for a
recursively subdivided icosahedron, 'oct#'
for a recursively
subdivided octahedron, or 'all'
for all points. In general, you can
speed up the plotting by selecting a sparser source space.
Defaults to ‘oct6’.
New in version 0.15.0.
str
| None
Title for the figure. If None, the subject name will be used.
New in version 0.17.0.
str
| float
If True, enable interactive picking of a point on the surface of the
brain and plot its time course.
This feature is only available with the PyVista 3d backend, and requires
time_viewer=True
. Defaults to ‘auto’, which will use True if and
only if time_viewer=True
, the backend is PyVista, and there is more
than one time point. If float (between zero and one), it specifies what
proportion of the total window should be devoted to traces (True is
equivalent to 0.25, i.e., it will occupy the bottom 1/4 of the figure).
New in version 0.20.0.
SourceSpaces
| None
The source space corresponding to the source estimate. Only necessary if the STC is a volume or mixed source estimate.
float
| dict
| None
Options for volumetric source estimate plotting, with key/value pairs:
'resolution'
float | NoneResolution (in mm) of volume rendering. Smaller (e.g., 1.) looks better at the cost of speed. None (default) uses the volume source space resolution, which is often something like 7 or 5 mm, without resampling.
'blending'
strCan be “mip” (default) for maximum intensity projection or “composite” for composite blending using alpha values.
'alpha'
float | NoneAlpha for the volumetric rendering. Defaults are 0.4 for vector source estimates and 1.0 for scalar source estimates.
'surface_alpha'
float | NoneAlpha for the surface enclosing the volume(s). None (default) will use half the volume alpha. Set to zero to avoid plotting the surface.
'silhouette_alpha'
float | NoneAlpha for a silhouette along the outside of the volume. None (default)
will use 0.25 * surface_alpha
.
'silhouette_linewidth'
floatThe line width to use for the silhouette. Default is 2.
A float input (default 1.) or None will be used for the 'resolution'
entry.
str
Can be “vertical” (default) or “horizontal”. When using “horizontal” mode, the PyVista backend must be used and hemi cannot be “split”.
dict
| None
Additional arguments to brain.add_data (e.g.,
dict(time_label_size=10)
).
dict
| None
Additional arguments to the mne.viz.Brain
constructor (e.g.,
dict(silhouette=True)
).
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.
mne.viz.Brain
| matplotlib.figure.Figure
An instance of mne.viz.Brain
or matplotlib figure.
Notes
Flatmaps are available by default for fsaverage
but not for other
subjects reconstructed by FreeSurfer. We recommend using
mne.compute_source_morph()
to morph source estimates to fsaverage
for flatmap plotting. If you want to construct your own flatmap for a given
subject, these links might help:
Examples using plot
:
Overview of MEG/EEG analysis with MNE-Python
Working with CTF data: the Brainstorm auditory dataset
The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Computing various MNE solutions
Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI
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
Corrupt known signal with point spread
Simulate raw data using subject anatomy
Make figures more publication ready
Sensitivity map of SSP projections
Compute Power Spectral Density of inverse solution from single epochs
Display sensitivity maps for EEG and MEG sensors
Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Generate a functional label from source estimates
Compute source power estimate by projecting the covariance with MNE
Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution
Optically pumped magnetometer (OPM) data
Resample data.
If appropriate, an anti-aliasing filter is applied before resampling. See Resampling and decimating data for more information.
float
New sample rate to use.
int
| str
Amount to pad the start and end of the data. Can also be “auto” to use a padding that will result in a power-of-two size (can be much faster).
str
| tuple
Window to use in resampling. See scipy.signal.resample()
.
int
| None
The number of jobs to run in parallel. If -1
, it is set
to the number of CPU cores. Requires the joblib
package.
None
(default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1
(sequential execution) unless the call is performed under
a joblib.parallel_backend()
context manager that sets another
value for n_jobs
.
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.
SourceEstimate
The resampled source estimate.
Notes
For some data, it may be more accurate to use npad=0 to reduce artifacts. This is dataset dependent – check your data!
Note that the sample rate of the original data is inferred from tstep.
Examples using resample
:
2 samples permutation test on source data with spatio-temporal clustering
Right hemisphere data.
Right hemisphere vertno.
Save the source estimates to a file.
str
The stem of the file name. The file names used for surface source spaces are obtained by adding “-lh.stc” and “-rh.stc” (or “-lh.w” and “-rh.w”) to the stem provided, for the left and the right hemisphere, respectively.
str
File format to use. Allowed values are “stc” (default), “w”, and “h5”. The “w” format only supports a single time point.
If True (default False), overwrite the destination file if it exists.
New in version 1.0.
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 save
:
Compute source power spectral density (PSD) in a label
Compute induced power in the source space with dSPM
Compute sLORETA inverse solution on raw data
Sample rate of the data.
Shape of the data.
Shift time scale in epoched or evoked data.
float
The (absolute or relative) time shift in seconds. If relative
is True, positive tshift increases the time value associated with
each sample, while negative tshift decreases it.
If True, increase or decrease time values by tshift
seconds.
Otherwise, shift the time values such that the time of the first
sample equals tshift
.
The modified instance.
Notes
This method allows you to shift the time values associated with each data sample by an arbitrary amount. It does not resample the signal or change the data values in any way.
Take the square root.
SourceEstimate
A copy of the SourceEstimate with sqrt(data).
Make a summary stc file with sum over time points.
SourceEstimate
| VectorSourceEstimate
The modified stc.
Examples using sum
:
Compute source power spectral density (PSD) of VectorView and OPM data
A timestamp for each sample.
Last time point.
The first timestamp.
Export data in tabular structure as a pandas DataFrame.
Vertices are converted to columns in the DataFrame. By default,
an additional column “time” is added, unless index='time'
(in which case time values form the DataFrame’s index).
None
Kind of index to use for the DataFrame. If None
, a sequential
integer index (pandas.RangeIndex
) will be used. If 'time'
, a
pandas.Float64Index
, pandas.Int64Index
, or
pandas.TimedeltaIndex
will be used
(depending on the value of time_format
).
Defaults to None
.
dict
| None
Scaling factor applied to the channels picked. If None
, defaults to
dict(eeg=1e6, mag=1e15, grad=1e13)
— i.e., converts EEG to µV,
magnetometers to fT, and gradiometers to fT/cm.
If True, the DataFrame is returned in long format where each row is one
observation of the signal at a unique combination of time point and vertex.
Defaults to False
.
str
| None
Desired time format. If None
, no conversion is applied, and time values
remain as float values in seconds. If 'ms'
, time values will be rounded
to the nearest millisecond and converted to integers. If 'timedelta'
,
time values will be converted to pandas.Timedelta
values.
Default is None
.
New in version 0.20.
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.
pandas.DataFrame
A dataframe suitable for usage with other statistical/plotting/analysis packages.
Get a source estimate from morphed source to the original subject.
SourceSpaces
The original source spaces that were morphed to the current subject.
str
| None
The original subject. For most source spaces this shouldn’t need to be provided, since it is stored in the source space itself.
None
The path to the directory containing the FreeSurfer subjects
reconstructions. If None
, defaults to the SUBJECTS_DIR
environment
variable.
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.
SourceEstimate
| VectorSourceEstimate
The transformed source estimate.
See also
Notes
New in version 0.10.0.
Examples using to_original_src
:
Apply linear transform.
The transform is applied to each source time course independently.
callable()
The transform to be applied, including parameters (see, e.g.,
functools.partial()
). The first parameter of the function is
the input data. The first two dimensions of the transformed data
should be (i) vertices and (ii) time. See Notes for details.
array
| None
Indices of source time courses for which to compute transform. If None, all time courses are used.
float
| int
| None
First time point to include (ms). If None, self.tmin is used.
float
| int
| None
Last time point to include (ms). If None, self.tmax is used.
If True, return a new instance of SourceEstimate instead of modifying the input inplace.
SourceEstimate
| VectorSourceEstimate
| list
The transformed stc or, in the case of transforms which yield N-dimensional output (where N > 2), a list of stcs. For a list, copy must be True.
Notes
Transforms which yield 3D output (e.g. time-frequency transforms) are valid, so long as the first two dimensions are vertices and time. In this case, the copy parameter must be True and a list of SourceEstimates, rather than a single instance of SourceEstimate, will be returned, one for each index of the 3rd dimension of the transformed data. In the case of transforms yielding 2D output (e.g. filtering), the user has the option of modifying the input inplace (copy = False) or returning a new instance of SourceEstimate (copy = True) with the transformed data.
Applying transforms can be significantly faster if the SourceEstimate object was created using “(kernel, sens_data)”, for the “data” parameter as the transform is applied in sensor space. Inverse methods, e.g., “apply_inverse_epochs”, or “apply_lcmv_epochs” do this automatically (if possible).
Get data after a linear (time) transform has been applied.
The transform is applied to each source time course independently.
callable()
The transform to be applied, including parameters (see, e.g.,
functools.partial()
). The first parameter of the function is
the input data. The first return value is the transformed data,
remaining outputs are ignored. The first dimension of the
transformed data has to be the same as the first dimension of the
input data.
array
| None
Indicices of source time courses for which to compute transform. If None, all time courses are used.
int
| None
Index of first time point to include. If None, the index of the first time point is used.
int
| None
Index of the first time point not to include. If None, time points up to (and including) the last time point are included.
ndarray
The transformed data.
Notes
Applying transforms can be significantly faster if the SourceEstimate object was created using “(kernel, sens_data)”, for the “data” parameter as the transform is applied in sensor space. Inverse methods, e.g., “apply_inverse_epochs”, or “apply_lcmv_epochs” do this automatically (if possible).
The change in time between two consecutive samples (1 / sfreq).
mne.SourceEstimate
#Overview of MEG/EEG analysis with MNE-Python
Working with CTF data: the Brainstorm auditory dataset
The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Computing various MNE solutions
Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI
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
Corrupt known signal with point spread
Generate simulated evoked data
Simulate raw data using subject anatomy
Cortical Signal Suppression (CSS) for removal of cortical signals
Make figures more publication ready
Sensitivity map of SSP projections
Compute Power Spectral Density of inverse solution from single epochs
Compute source power spectral density (PSD) in a label
Compute source power spectral density (PSD) of VectorView and OPM data
Compute induced power in the source space with dSPM
Display sensitivity maps for EEG and MEG sensors
Compute MNE-dSPM inverse solution on single epochs
Compute sLORETA inverse solution on raw data
Source localization with a custom inverse solver
Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Extracting time course from source_estimate object
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 source power estimate by projecting the covariance with MNE
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution
Optically pumped magnetometer (OPM) data