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.

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

1(1,2): Zoltan J. Koles, Michael S. Lazar, and Steven Z. Zhou. Spatial patterns underlying population differences in the background EEG. Brain Topography, 2(4):275–284, 1990. doi:10.1007/BF01129656.
2(1,2,3): Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, and Klaus-Robert Müller. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine, 25(1):41–56, 2008. doi:10.1109/MSP.2008.4408441.
3(1,2): Moritz Grosse-Wentrup and Martin Buss. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Transactions on Biomedical Engineering, 55(8):1991–2000, 2008. doi:10.1109/TBME.2008.921154.
4: Alexandre Barachant, Stephane Bonnet, Marco Congedo, and Christian Jutten. Common spatial pattern revisited by Riemannian geometry. In 2010 IEEE International Workshop on Multimedia Signal Processing, 472–476. 2010. doi:10.1109/MMSP.2010.5662067.

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

`__hash__`(/)	Return hash(self).
`fit`(X, y)	Estimate the CSP 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 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:

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.

Parameters

Xarray, shape (n_samples, n_features): Training set.
yarray, shape (n_samples,): Target values.
**fit_paramsdict: Additional fitting parameters passed to self.fit.

Returns

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

Examples using fit_transform:

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

paramsmapping of str to any: 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='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.

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. All matplotlib options are accepted.

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:

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.

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_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

contoursint | array of float

image_interpstr

The image interpolation to be used. All matplotlib options are accepted.

averagefloat | None

spherefloat | 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.

Returns

figinstance of matplotlib.figure.Figure: The figure.

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 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: