mne.decoding.CSP

class mne.decoding.CSP(n_components=4, reg=None, log=True, cov_est='concat')

M/EEG signal decomposition using the Common Spatial Patterns (CSP).

This object can be used as a supervised decomposition to estimate spatial filters for feature extraction in a 2 class decoding problem. CSP in the context of EEG was first described in [1]; a comprehensive tutorial on CSP can be found in [2].

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 (default None)

if not None, allow regularization for covariance estimation if float, shrinkage covariance is used (0 <= shrinkage <= 1). if str, optimal shrinkage using Ledoit-Wolf Shrinkage (‘ledoit_wolf’) or Oracle Approximating Shrinkage (‘oas’).

log : bool (default True)

If true, apply log to standardize the features. If false, features are just z-scored.

cov_est : str (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.

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_channels,)

If fit, the mean squared power for each component.

std_ : ndarray, shape (n_channels,)

If fit, the std squared power for each component.

References

[1] Zoltan J. Koles, Michael S. Lazar, Steven Z. Zhou. Spatial Patterns
Underlying Population Differences in the Background EEG. Brain Topography 2(4), 275-284, 1990.
[2] Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe,
Klaus-Robert Müller. Optimizing Spatial Filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine 25(1), 41-56, 2008.

Methods

fit(epochs_data, y) Estimate the CSP decomposition on epochs.
fit_transform(X[, y]) Fit to data, then transform it
get_params([deep]) Return all parameters (mimics sklearn API).
plot_filters(info[, components, ch_type, ...]) Plot topographic filters of CSP components.
plot_patterns(info[, components, ch_type, ...]) Plot topographic patterns of CSP components.
set_params(**params) Set parameters (mimics sklearn API).
transform(epochs_data[, y]) Estimate epochs sources given the CSP filters.
__init__(n_components=4, reg=None, log=True, cov_est='concat')

Init of CSP.

fit(epochs_data, y)

Estimate the CSP decomposition on epochs.

Parameters:

epochs_data : ndarray, shape (n_epochs, n_channels, n_times)

The data to estimate the CSP on.

y : array, shape (n_epochs,)

The class for each epoch.

Returns:

self : instance of CSP

Returns the modified instance.

fit_transform(X, y=None, **fit_params)

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:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Return all parameters (mimics sklearn API).

Parameters:

deep: boolean, optional :

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

plot_filters(info, components=None, ch_type=None, layout=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scale=None, scale_time=1, unit=None, res=64, size=1, cbar_fmt='%3.1f', name_format='CSP%01d', proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', average=None, head_pos=None)

Plot topographic filters of CSP components.

The CSP filters are used to extract discriminant neural sources from the measured data (a.k.a. the backward model).

Parameters:

info : instance of Info

Info dictionary of the epochs used to fit CSP. If not possible, consider using create_info.

components : float | array of floats | None.

The CSP 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 channels are chosen in the order given above.

layout : None | Layout

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found the layout is automatically generated from the sensor locations.

vmin : float | callable

The value specfying 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 specfying 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).

cmap : matplotlib colormap

Colormap. For magnetometers and eeg defaults to ‘RdBu_r’, else ‘Reds’.

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.

scale : dict | float | None

Scale the data for plotting. If None, defaults to 1e6 for eeg, 1e13 for grad and 1e15 for mag.

scale_time : float | None

Scale the time labels. Defaults to 1.

unit : 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”

proj : bool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be show.

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 of bool, shape (n_channels, n_times) | None

The channels to be marked as significant at a given time point. Indicies 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, and the ‘autoshrink’ (bool) field will trigger automated shrinking of the positions due to points outside the outline. 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 | False | None

The number of contour lines to draw. If 0, no contours will be drawn.

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.

head_pos : dict | None

If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’ (tuple) for what the center and scale of the head should be relative to the electrode locations.

Returns:

fig : instance of matplotlib.figure.Figure

The figure.

plot_patterns(info, components=None, ch_type=None, layout=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scale=None, scale_time=1, unit=None, res=64, size=1, cbar_fmt='%3.1f', name_format='CSP%01d', proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', average=None, head_pos=None)

Plot topographic patterns of CSP components.

The CSP patterns explain how the measured data was generated from the neural sources (a.k.a. the forward model).

Parameters:

info : instance of Info

Info dictionary of the epochs used to fit CSP. If not possible, consider using create_info.

components : float | array of floats | None.

The CSP 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 channels are chosen in the order given above.

layout : None | Layout

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found the layout is automatically generated from the sensor locations.

vmin : float | callable

The value specfying 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 specfying 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).

cmap : matplotlib colormap

Colormap. For magnetometers and eeg defaults to ‘RdBu_r’, else ‘Reds’.

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.

scale : dict | float | None

Scale the data for plotting. If None, defaults to 1e6 for eeg, 1e13 for grad and 1e15 for mag.

scale_time : float | None

Scale the time labels. Defaults to 1.

unit : 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”

proj : bool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be show.

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 of bool, shape (n_channels, n_times) | None

The channels to be marked as significant at a given time point. Indicies 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, and the ‘autoshrink’ (bool) field will trigger automated shrinking of the positions due to points outside the outline. 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 | False | None

The number of contour lines to draw. If 0, no contours will be drawn.

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.

head_pos : dict | None

If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’ (tuple) for what the center and scale of the head should be relative to the electrode locations.

Returns:

fig : instance of matplotlib.figure.Figure

The figure.

set_params(**params)

Set parameters (mimics sklearn API).

transform(epochs_data, y=None)

Estimate epochs sources given the CSP filters.

Parameters:

epochs_data : array, shape (n_epochs, n_channels, n_times)

The data.

y : None

Not used.

Returns:

X : ndarray of shape (n_epochs, n_sources)

The CSP features averaged over time.