This documentation is for development version 0.18.dev0.

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 (default None)

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 to method to mne.compute_covariance().

log : None | bool (default None)

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 | int | dict | ‘full’

See mne.compute_covariance().

New in version 0.17.

References

[1](1, 2) 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.

Methods

__hash__($self, /) Return hash(self).
fit(X, y) Estimate the SPoC decomposition on epochs.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
plot_filters(info[, components, ch_type, …]) Plot topographic filters of components.
plot_patterns(info[, components, ch_type, …]) Plot topographic patterns of components.
set_params(**params) Set the parameters of this estimator.
transform(X) Estimate epochs sources given the SPoC filters.
__hash__($self, /)

Return hash(self).

fit(X, y)[source]

Estimate the SPoC decomposition on epochs.

Parameters:
X : ndarray, shape (n_epochs, n_channels, n_times)

The data on which to estimate the SPoC.

y : array, shape (n_epochs,)

The class for each epoch.

Returns:
self : instance of SPoC

Returns the modified instance.

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:
X : array, shape (n_samples, n_features)

Training set.

y : array, shape (n_samples,)

Target values.

Returns:
X_new : array, shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

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

Returns:
params : mapping of string to any

Parameter names mapped to their values.

plot_filters(info, components=None, ch_type=None, layout=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, head_pos=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 : instance of Info

Info dictionary of the epochs used for fitting. If not possible, consider using create_info.

components : float | array of float | 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.

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 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).

cmap : matplotlib 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 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, 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 | 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_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, 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, head_pos=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 : instance of Info

Info dictionary of the epochs used for fitting. If not possible, consider using create_info.

components : float | array of float | 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.

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 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).

cmap : matplotlib 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 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, 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 | 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_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)[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.

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)