pyriemann.spatialfilters.SPoC

class pyriemann.spatialfilters.SPoC(nfilter=4, metric='euclid', log=True)

SPoC spatial filtering with covariance matrices as inputs.

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 comodulates with the target variable.

SPoC can be seen as an extension of the pyriemann.spatialfilters.CSP driven by a continuous variable rather than a discrete (often binary) variable. Typical applications include extraction of motor patterns using EMG power or audio paterns using sound envelope.

Parameters
nfilterint, default=4

The number of components to decompose M/EEG signals.

metricstr, default=’euclid’

The metric for the estimation of mean covariance matrices.

logbool, default=True

If true, return the log variance, otherwise return the spatially filtered covariance matrices.

See also

CSP

Notes

New in version 0.2.4.

References

1

SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters S. Dahne, F. C. Meinecke, S. Haufe, J. Hohne, M. Tangermann, K-R. Muller, and V. V. Nikulin. NeuroImage, 86, 111-122, 2014.

Attributes
filters_ndarray, shape (min(n_channels, n_filters), n_channels)

If fit, the SPoC spatial filters.

patterns_ndarray, shape (min(n_channels, n_filters), n_channels)

If fit, the SPoC spatial patterns.

__init__(nfilter=4, metric='euclid', log=True)

Init.

fit(X, y)

Train spatial filters.

Parameters
Xndarray, shape (n_trials, n_channels, n_channels)

Set of covariance matrices.

yndarray, shape (n_trials,)

Target variable for each trial.

Returns
selfSPoC instance

The SPoC 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
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters
deepbool, default=True

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

Returns
paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X)

Apply spatial filters.

Parameters
Xndarray, shape (n_trials, n_channels, n_channels)

Set of covariance matrices.

Returns
Xfndarray, shape (n_trials, n_filters) or ndarray, shape (n_trials, n_filters, n_filters)

Set of spatialy filtered log-variance or covariance, depending on the ‘log’ input parameter.