pyriemann.estimation.HankelCovariances

class pyriemann.estimation.HankelCovariances(delays=4, estimator='scm', **kwds)

Estimation of covariance matrix with time delayed Hankel matrices.

Hankel covariance matrices [1] are useful to catch spectral dynamics of the signal, similarly to the CSSP method [2]. It is done by concatenating time delayed version of the signal before covariance estimation.

Parameters:
delaysint | list of int, default=4

The delays to apply for the Hankel matrices. If int, it use a range of delays up to the given value. A list of int can be given.

estimatorstring, default=’scm’

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

**kwdsdict

Any further parameters are passed directly to the covariance estimator.

See also

Covariances
ERPCovariances
CospCovariances

References

[2]

Spatio-spectral filters for improving the classification of single trial EEG S. Lemm, B. Blankertz, B. Curio, K-R. Muller. IEEE Transactions on Biomedical Engineering 52(9), 1541-1548, 2005.

__init__(delays=4, estimator='scm', **kwds)

Init.

fit(X, y=None)

Fit.

Do nothing. For compatibility purpose.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

yNone

Not used, here for compatibility with sklearn API.

Returns:
selfHankelCovariances instance

The HankelCovariances 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 the Hankel covariance matrices.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

Returns:
covmatsndarray, shape (n_matrices, n_delays x n_channels, n_delays x n_channels)

Hankel covariance matrices, where n_delays is equal to delays when it is a int, and to 1 + len(delays) when it is a list.