Implementation of the SPoC spatial filtering.
Source Power Comodulation (SPoC) [1] allows to extract spatial filters and patterns by using a target (continuous) variable in the decomposition process in order to give preference to components whose power correlates with the target variable.
SPoC can be seen as an extension of the CSP driven by a continuous variable rather than a discrete variable. Typical applications include extraction of motor patterns using EMG power or audio patterns using sound envelope.
int
The number of components to decompose M/EEG signals.
float
| str
| None
(default None
)If not None (same as 'empirical'
, default), allow
regularization for covariance estimation.
If float, shrinkage is used (0 <= shrinkage <= 1).
For str options, reg
will be passed to method
to
mne.compute_covariance()
.
None
| bool (default None
)If transform_into == ‘average_power’ and log is None or True, then applies a log transform to standardize the features, else the features are z-scored. If transform_into == ‘csp_space’, then log must be None.
If ‘average_power’ then self.transform will return the average power of each spatial filter. If ‘csp_space’ self.transform will return the data in CSP space. Defaults to ‘average_power’.
dict
| None
Parameters to pass to mne.compute_covariance()
.
New in version 0.16.
None
| ‘info’ | ‘full’ | dict
This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).
None
The rank will be estimated from the data after proper scaling of different channel types.
'info'
The rank is inferred from info
. If data have been processed
with Maxwell filtering, the Maxwell filtering header is used.
Otherwise, the channel counts themselves are used.
In both cases, the number of projectors is subtracted from
the (effective) number of channels in the data.
For example, if Maxwell filtering reduces the rank to 68, with
two projectors the returned value will be 66.
'full'
The rank is assumed to be full, i.e. equal to the
number of good channels. If a Covariance
is passed, this can
make sense if it has been (possibly improperly) regularized without
taking into account the true data rank.
dict
Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.
This parameter must be a dictionary whose keys correspond to
channel types in the data (e.g. 'meg'
, 'mag'
, 'grad'
,
'eeg'
), and whose values are integers representing the
respective ranks. For example, {'mag': 90, 'eeg': 45}
will assume
a rank of 90
and 45
for magnetometer data and EEG data,
respectively.
The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.
The default is None
.
New in version 0.17.
See also
References
ndarray
, shape (n_channels, n_channels)If fit, the SPoC spatial filters, else None.
ndarray
, shape (n_channels, n_channels)If fit, the SPoC spatial patterns, else None.
ndarray
, shape (n_components,)If fit, the mean squared power for each component.
ndarray
, shape (n_components,)If fit, the std squared power for each component.
Methods
|
Estimate the SPoC decomposition on epochs. |
|
Fit to data, then transform it. |
|
Get parameters for this estimator. |
|
Plot topographic filters of components. |
|
Plot topographic patterns of components. |
|
Set the parameters of this estimator. |
|
Estimate epochs sources given the SPoC filters. |
Estimate the SPoC decomposition on epochs.
SPoC
Returns the modified instance.
Examples using fit
:
Continuous Target Decoding with SPoC
Fit to data, then transform it.
Fits transformer to X
and y
with optional parameters
fit_params
, and returns a transformed version of X
.
Plot topographic filters of components.
The filters are used to extract discriminant neural sources from the measured data (a.k.a. the backward model).
mne.Info
The mne.Info
object with information about the sensors and methods of measurement. Used for fitting. If not available, consider using
mne.create_info()
.
float
| array
of float
| None
The patterns to plot. If None, n_components will be shown.
None
The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None.
float
| callable()
The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data).
float
| callable()
The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If vmin is None, but vmax is not, defaults to np.min(data). If callable, the output equals vmax(data).
None
Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to (None, True). Defaults to ‘RdBu_r’.
Warning
Interactive mode works smoothly only for a small amount of topomaps.
str
Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True, a circle will be used (via .add_artist). Defaults to True.
Plot a colorbar.
dict
| float
| None
The scalings of the channel types to be applied for plotting.
If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15)
.
dict
| str
| None
The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.
int
The resolution of the topomap image (n pixels along each side).
float
Side length per topomap in inches.
str
String format for colorbar values.
str
String format for topomap values. Defaults to “CSP%01d”.
Show figure if True.
callable()
If True, show channel names on top of the map. If a callable is
passed, channel names will be formatted using the callable; e.g.,
to delete the prefix ‘MEG ‘ from all channel names, pass the
function lambda x: x.replace(‘MEG ‘, ‘’). If mask
is not None,
only significant sensors will be shown.
str
| None
Title. If None (default), no title is displayed.
ndarray
of bool, shape (n_channels, n_times) | None
The channels to be marked as significant at a given time point.
Indices set to True
will be considered. Defaults to None.
dict
| None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
dict
| None
The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.
int
| array
of float
The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6.
str
The image interpolation to be used. Options are 'cubic'
(default)
to use scipy.interpolate.CloughTocher2DInterpolator
,
'nearest'
to use scipy.spatial.Voronoi
or
'linear'
to use scipy.interpolate.LinearNDInterpolator
.
float
| None
The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging.
matplotlib.figure.Figure
The figure.
Plot topographic patterns of components.
The patterns explain how the measured data was generated from the neural sources (a.k.a. the forward model).
mne.Info
The mne.Info
object with information about the sensors and methods of measurement. Used for fitting. If not available, consider using
mne.create_info()
.
float
| array
of float
| None
The patterns to plot. If None, n_components will be shown.
None
The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None.
float
| callable()
The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data).
float
| callable()
The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If vmin is None, but vmax is not, default np.min(data). If callable, the output equals vmax(data).
None
Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to (None, True). Defaults to ‘RdBu_r’.
Warning
Interactive mode works smoothly only for a small amount of topomaps.
str
Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True, a circle will be used (via .add_artist). Defaults to True.
Plot a colorbar.
dict
| float
| None
The scalings of the channel types to be applied for plotting.
If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15)
.
dict
| str
| None
The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.
int
The resolution of the topomap image (n pixels along each side).
float
Side length per topomap in inches.
str
String format for colorbar values.
str
String format for topomap values. Defaults to “CSP%01d”.
Show figure if True.
callable()
If True, show channel names on top of the map. If a callable is
passed, channel names will be formatted using the callable; e.g.,
to delete the prefix ‘MEG ‘ from all channel names, pass the
function lambda x: x.replace(‘MEG ‘, ‘’). If mask
is not None,
only significant sensors will be shown.
str
| None
Title. If None (default), no title is displayed.
ndarray
of bool, shape (n_channels, n_patterns) | None
Array indicating channel-pattern combinations to highlight with a distinct
plotting style. Array elements set to True
will be plotted
with the parameters given in mask_params
. Defaults to None
,
equivalent to an array of all False
elements.
dict
| None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
dict
| None
The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.
int
| array
of float
The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6.
str
The image interpolation to be used. Options are 'cubic'
(default)
to use scipy.interpolate.CloughTocher2DInterpolator
,
'nearest'
to use scipy.spatial.Voronoi
or
'linear'
to use scipy.interpolate.LinearNDInterpolator
.
float
| None
The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging.
float
| 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. Currently the head radius
does not affect plotting.
New in version 0.20.
Changed in version 1.1: Added 'eeglab'
option.
matplotlib.figure.Figure
The figure.
Examples using plot_patterns
:
Continuous Target Decoding with SPoC
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
dict
Parameters.
The object.
Estimate epochs sources given the SPoC filters.
array
, shape (n_epochs, n_channels, n_times)The data.
ndarray
If self.transform_into == ‘average_power’ then returns the power of CSP features averaged over time and shape (n_epochs, n_sources) If self.transform_into == ‘csp_space’ then returns the data in CSP space and shape is (n_epochs, n_sources, n_times).
mne.decoding.SPoC
#Continuous Target Decoding with SPoC