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: 


See also
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, 111122. 
Attributes: 


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: 


Returns: 

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: 


Returns: 

get_params
(deep=True)[source]¶Get parameters for this estimator.
Parameters: 


Returns: 

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: 


Returns: 

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: 


Returns: 

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: 


Returns: 
