mne.decoding.SPoC¶
-
class
mne.decoding.
SPoC
(n_components=4, reg=None, log=None, transform_into='average_power', cov_method_params=None, rank=None)[source]¶ 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.
- Parameters
- n_components
int
The number of components to decompose M/EEG signals.
- reg
float
|str
|None
(defaultNone
) 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 tomethod
tomne.compute_covariance()
.- log
None
| bool (defaultNone
) 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.
- transform_into{‘average_power’, ‘csp_space’}
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’.
- cov_method_params
dict
|None
Parameters to pass to
mne.compute_covariance()
.New in version 0.16.
- rank
None
|dict
| ‘info’ | ‘full’ This controls the rank computation that can be read from the measurement info or estimated from the data. See
Notes
ofmne.compute_rank()
for details.The default is None.New in version 0.17.
- n_components
See also
References
- 1
Dahne, S., Meinecke, F. C., Haufe, S., Hohne, J., Tangermann, M., Muller, K. R., & Nikulin, V. V. (2014). SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters. NeuroImage, 86, 111-122.
- Attributes
- filters_
ndarray
, shape (n_channels, n_channels) If fit, the SPoC spatial filters, else None.
- patterns_
ndarray
, shape (n_channels, n_channels) If fit, the SPoC spatial patterns, else None.
- mean_
ndarray
, shape (n_components,) If fit, the mean squared power for each component.
- std_
ndarray
, shape (n_components,) If fit, the std squared power for each component.
- filters_
Methods
__hash__
(/)Return hash(self).
fit
(X, y)Estimate the SPoC decomposition on epochs.
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])Get parameters for this estimator.
plot_filters
(info[, components, ch_type, …])Plot topographic filters of components.
plot_patterns
(info[, components, ch_type, …])Plot topographic patterns of components.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Estimate epochs sources given the SPoC filters.
-
fit
(X, y)[source]¶ Estimate the SPoC decomposition on epochs.
- Parameters
- Returns
- selfinstance of
SPoC
Returns the modified instance.
- selfinstance of
Examples using
fit
:
-
fit_transform
(X, y=None, **fit_params)[source]¶ 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_filters
(info, components=None, ch_type=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scalings=None, units='a.u.', res=64, size=1, cbar_fmt='%3.1f', name_format='CSP%01d', show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', average=None)[source]¶ Plot topographic filters of components.
The filters are used to extract discriminant neural sources from the measured data (a.k.a. the backward model).
- Parameters
- infoinstance of
Info
Info dictionary of the epochs used for fitting. If not possible, consider using
create_info
.- components
float
|array
offloat
|None
The patterns to plot. If None, n_components will be shown.
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
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.
- vmin
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).
- vmax
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).
- cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ |
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.
- sensorsbool |
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.
- colorbarbool
Plot a colorbar.
- scalings
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)
.- units
dict
|str
|None
The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.
- res
int
The resolution of the topomap image (n pixels along each side).
- size
float
Side length per topomap in inches.
- cbar_fmt
str
String format for colorbar values.
- name_format
str
String format for topomap values. Defaults to “CSP%01d”.
- showbool
Show figure if True.
- show_namesbool |
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.- title
str
|None
Title. If None (default), no title is displayed.
- mask
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.- mask_params
dict
|None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4)
- outlines‘head’ | ‘skirt’ |
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’.
- contours
int
|array
offloat
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.
- image_interp
str
The image interpolation to be used. All matplotlib options are accepted.
- average
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.
- infoinstance of
- Returns
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
-
plot_patterns
(info, components=None, ch_type=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scalings=None, units='a.u.', res=64, size=1, cbar_fmt='%3.1f', name_format='CSP%01d', show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', average=None, sphere=None)[source]¶ Plot topographic patterns of components.
The patterns explain how the measured data was generated from the neural sources (a.k.a. the forward model).
- Parameters
- infoinstance of
Info
Info dictionary of the epochs used for fitting. If not possible, consider using
create_info
.- components
float
|array
offloat
|None
The patterns to plot. If None, n_components will be shown.
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
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.
- vmin
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).
- vmax
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).
- cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ |
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.
- sensorsbool |
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.
- colorbarbool
Plot a colorbar.
- scalings
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)
.- units
dict
|str
|None
The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.
- res
int
The resolution of the topomap image (n pixels along each side).
- size
float
Side length per topomap in inches.
- cbar_fmt
str
String format for colorbar values.
- name_format
str
String format for topomap values. Defaults to “CSP%01d”.
- showbool
Show figure if True.
- show_namesbool |
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.- title
str
|None
Title. If None (default), no title is displayed.
- mask
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.- mask_params
dict
|None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4)
- outlines‘head’ | ‘skirt’ |
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’.
- contours
int
|array
offloat
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.
- image_interp
str
The image interpolation to be used. All matplotlib options are accepted.
- average
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.
- sphere
float
| array_like |str
|None
The sphere parameters to use for the cartoon head. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give the radius (origin assumed 0, 0, 0). Can also be a spherical ConductorModel, which will use the origin and radius. Can be “auto” to use a digitization-based fit. Can also be None (default) to use ‘auto’ when enough extra digitization points are available, and 0.095 otherwise. Currently the head radius does not affect plotting.
New in version 0.20.
- infoinstance of
- Returns
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
Examples using
plot_patterns
:
-
set_params
(**params)[source]¶ 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. Returns ——- self
-
transform
(X)[source]¶ Estimate epochs sources given the SPoC filters.
- Parameters
- X
array
, shape (n_epochs, n_channels, n_times) The data.
- X
- Returns
- X
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).
- X