mne.decoding.CSP#
- class mne.decoding.CSP(n_components=4, reg=None, log=None, cov_est='concat', transform_into='average_power', norm_trace=False, cov_method_params=None, rank=None, component_order='mutual_info')[source]#
M/EEG signal decomposition using the Common Spatial Patterns (CSP).
This class can be used as a supervised decomposition to estimate spatial filters for feature extraction. CSP in the context of EEG was first described in [1]; a comprehensive tutorial on CSP can be found in [2]. Multi-class solving is implemented from [3].
- Parameters:
- n_components
int
(default 4) The number of components to decompose M/EEG signals. This number should be set by cross-validation.
- reg
float
|str
|None
(defaultNone
) If not None (same as
'empirical'
, default), allow regularization for covariance estimation. If float (between 0 and 1), shrinkage is used. For str values,reg
will be passed asmethod
tomne.compute_covariance()
.- log
None
|bool
(defaultNone
) If
transform_into
equals'average_power'
andlog
is None or True, then apply a log transform to standardize features, else features are z-scored. Iftransform_into
is'csp_space'
,log
must be None.- cov_est‘concat’ | ‘epoch’ (default ‘concat’)
If
'concat'
, covariance matrices are estimated on concatenated epochs for each class. If'epoch'
, covariance matrices are estimated on each epoch separately and then averaged over each class.- transform_into‘average_power’ | ‘csp_space’ (default ‘average_power’)
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.- norm_trace
bool
(defaultFalse
) Normalize class covariance by its trace. Trace normalization is a step of the original CSP algorithm [1] to eliminate magnitude variations in the EEG between individuals. It is not applied in more recent work [2], [3] and can have a negative impact on pattern order.
- cov_method_params
dict
|None
Parameters to pass to
mne.compute_covariance()
.New in version 0.16.
- rank
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 of90
and45
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.
- component_order‘mutual_info’ | ‘alternate’ (default ‘mutual_info’)
If
'mutual_info'
order components by decreasing mutual information (in the two-class case this uses a simplification which orders components by decreasing absolute deviation of the eigenvalues from 0.5 [4]). For the two-class case,'alternate'
orders components by starting with the largest eigenvalue, followed by the smallest, the second-to-largest, the second-to-smallest, and so on [2].New in version 0.21.
- n_components
See also
References
- Attributes:
- filters_
ndarray
, shape (n_channels, n_channels) If fit, the CSP components used to decompose the data, else None.
- patterns_
ndarray
, shape (n_channels, n_channels) If fit, the CSP patterns used to restore M/EEG signals, 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
fit
(X, y)Estimate the CSP decomposition on epochs.
fit_transform
(X, y, **fit_params)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 CSP filters.
- fit(X, y)[source]#
Estimate the CSP decomposition on epochs.
- Parameters:
- Returns:
- selfinstance of
CSP
Returns the modified instance.
- selfinstance of
Examples using
fit
:Decoding (MVPA)Continuous Target Decoding with SPoC
Continuous Target Decoding with SPoC
- fit_transform(X, y, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
, and returns a transformed version ofX
.- Parameters:
- Returns:
- X_new
array
, shape (n_samples, n_features_new) Transformed array.
- X_new
Examples using
fit_transform
:Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
- 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='cubic', 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:
- info
mne.Info
The
mne.Info
object with information about the sensors and methods of measurement. Used for fitting. If not available, consider usingmne.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.
- sensors
bool
|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.
- colorbar
bool
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”.
- show
bool
Show figure if True.
- show_names
bool
|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
ofbool
, 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’.
Deprecated since version v1.2: The
outlines='skirt'
option is no longer supported and will raise an error starting in version 1.3. Passoutlines='head', sphere='eeglab'
for similar behavior.- 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. Options are
'cubic'
(default) to usescipy.interpolate.CloughTocher2DInterpolator
,'nearest'
to usescipy.spatial.Voronoi
or'linear'
to usescipy.interpolate.LinearNDInterpolator
.- 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.
- info
- Returns:
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
Examples using
plot_filters
:Decoding (MVPA)
- 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='cubic', 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:
- info
mne.Info
The
mne.Info
object with information about the sensors and methods of measurement. Used for fitting. If not available, consider usingmne.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.
- sensors
bool
|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.
- colorbar
bool
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”.
- show
bool
Show figure if True.
- show_names
bool
|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
ofbool
, 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 inmask_params
. Defaults toNone
, equivalent to an array of allFalse
elements.- 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’.
Deprecated since version v1.2: The
outlines='skirt'
option is no longer supported and will raise an error starting in version 1.3. Passoutlines='head', sphere='eeglab'
for similar behavior.- 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. Options are
'cubic'
(default) to usescipy.interpolate.CloughTocher2DInterpolator
,'nearest'
to usescipy.spatial.Voronoi
or'linear'
to usescipy.interpolate.LinearNDInterpolator
.- 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 | instance ofConductorModel
|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.New in version 0.20.
Changed in version 1.1: Added
'eeglab'
option.
- info
- Returns:
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
Examples using
plot_patterns
:Decoding (MVPA)Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)Continuous Target Decoding with SPoC
Continuous Target Decoding with SPoC
- 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.- Parameters:
- **params
dict
Parameters.
- **params
- Returns:
- instinstance
The object.
- transform(X)[source]#
Estimate epochs sources given the CSP 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
Examples using
transform
:Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Examples using mne.decoding.CSP
#
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Decoding in time-frequency space using Common Spatial Patterns (CSP)
Continuous Target Decoding with SPoC