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_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

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_output(*, transform=None)

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

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.