pyriemann.estimation.ERPCovariances

class pyriemann.estimation.ERPCovariances(classes=None, estimator='scm', svd=None, **kwds)

Estimate special form covariance matrices for ERP.

Estimation of special form covariance matrix dedicated to event-related potentials (ERP) processing. For each class, a prototyped response is obtained by average across trials:

\[\mathbf{P} = \frac{1}{m} \sum_{i=1}^{m} \mathbf{X}_i\]

and a super trial is built using the concatenation of \(\mathbf{P}\) and the trial \(\mathbf{X}_i\):

\[\begin{split}\mathbf{\tilde{X}}_i = \left[ \begin{array}{c} \mathbf{P} \\ \mathbf{X}_i \end{array} \right]\end{split}\]

This super trial \(\mathbf{\tilde{X}}_i\) will be used for covariance estimation. This allows to take into account the spatial structure of the signal, as described in [1].

Parameters:

classeslist of int | None, default=None

List of classes to take into account for prototype estimation. If None, all classes will be accounted.

estimatorstring, default=’scm’

Covariance matrix estimator, see pyriemann.utils.covariance.covariances().

svdint | None, default=None

If not None, number of components of SVD used to reduce prototype responses.

**kwdsdict

Any further parameters are passed directly to the covariance estimator.

See also

Covariances

XdawnCovariances

References

[1]

A Plug and Play P300 BCI Using Information Geometry A. Barachant, M. Congedo. Research report, 2014.

[2]

A New generation of Brain-Computer Interface Based on Riemannian Geometry M. Congedo, A. Barachant, A. Andreev. Research report, 2013.

[3]

Classification de potentiels evoques P300 par geometrie riemannienne pour les interfaces cerveau-machine EEG A. Barachant, M. Congedo, G. van Veen, and C. Jutten, 24eme colloque GRETSI, 2013.

Attributes:

P_ndarray, shape (n_components, n_times)

If fit, prototyped responses for each class, where n_components is equal to n_classes x n_channels if svd is None, and to n_classes x min(svd, n_channels) otherwise.

__init__(classes=None, estimator='scm', svd=None, **kwds)

Init.

fit(X, y)

Fit.

Estimate the prototyped responses for each class.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

yndarray, shape (n_matrices,)

Labels for each matrix.

Returns:

selfERPCovariances instance

The ERPCovariances 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)

Estimate special form covariance matrices.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

Returns:

covmatsndarray, shape (n_matrices, n_components, n_components)

Covariance matrices for ERP, where the size of matrices n_components is equal to (1 + n_classes) x n_channels if svd is None, and to n_channels + n_classes x min(svd, n_channels) otherwise.