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_componentsint (default 4)

The number of components to decompose M/EEG signals. This number should be set by cross-validation.

regfloat | str | None (default None)

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 as method to mne.compute_covariance().

logNone | bool (default None)

If transform_into equals 'average_power' and log is None or True, then apply a log transform to standardize features, else features are z-scored. If transform_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_tracebool (default False)

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_paramsdict | None

Parameters to pass to mne.compute_covariance().

New in version 0.16.

rankNone | ‘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.

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.

See also

mne.preprocessing.Xdawn, SPoC

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.

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:

Xndarray, shape (n_epochs, n_channels, n_times): The data on which to estimate the CSP.
yarray, shape (n_epochs,): The class for each epoch.

Returns:

selfinstance of CSP: Returns the modified instance.

Examples using fit:

Decoding (MVPA)

Continuous Target Decoding with SPoC

fit_transform(X, y, **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.

Parameters:

Xarray, shape (n_samples, n_features): Training set.
yarray, shape (n_samples,): Target values or class labels.
**fit_paramsdict: Additional fitting parameters passed to the fit method..

Returns:

X_newarray, shape (n_samples, n_features_new): Transformed array.

Examples using fit_transform:

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, optional: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

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:

infomne.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().

componentsfloat | array of float | None

The patterns to plot. If None, n_components will be shown.

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.

vminfloat | 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).

vmaxfloat | 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.

scalingsdict | float | None

The scalings of the channel types to be applied for plotting. If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15).

unitsdict | str | None

The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.

resint

The resolution of the topomap image (n pixels along each side).

sizefloat

Side length per topomap in inches.

cbar_fmtstr

String format for colorbar values.

name_formatstr

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.

titlestr | None

Title. If None (default), no title is displayed.

maskndarray 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_paramsdict | 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’.

contoursint | 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.

image_interpstr

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.

averagefloat | 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.

Returns:

figinstance of matplotlib.figure.Figure: The figure.

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:

infomne.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().

componentsfloat | array of float | None

The patterns to plot. If None, n_components will be shown.

vminfloat | 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).

vmaxfloat | 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

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.

scalingsdict | float | None

The scalings of the channel types to be applied for plotting. If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15).

unitsdict | str | None

The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.

resint

The resolution of the topomap image (n pixels along each side).

sizefloat

Side length per topomap in inches.

cbar_fmtstr

String format for colorbar values.

name_formatstr

String format for topomap values. Defaults to “CSP%01d”.

showbool

Show figure if True.

show_namesbool | callable()

titlestr | None

Title. If None (default), no title is displayed.

maskndarray 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.

mask_paramsdict | 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

contoursint | array of float

image_interpstr

averagefloat | None

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.

Returns:

figinstance of matplotlib.figure.Figure: The figure.

Examples using plot_patterns:

Decoding (MVPA)

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

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:

**paramsdict: Parameters.

Returns:

instinstance: The object.

transform(X)[source]#

Estimate epochs sources given the CSP filters.

Parameters:

Xarray, shape (n_epochs, n_channels, n_times): The data.

Returns:

Xndarray: 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).

Examples using transform:

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Examples using `mne.decoding.CSP`#

Decoding (MVPA)

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

mne.decoding.CSP#

Examples using mne.decoding.CSP#

Examples using `mne.decoding.CSP`#