pyriemann.estimation.CrossSpectra

class pyriemann.estimation.CrossSpectra(window=128, overlap=0.75, fmin=None, fmax=None, fs=None)

Estimation of cross-spectral matrices.

Complex cross-spectral matrices are HPD matrices estimated as the spectrum covariance in the frequency domain [1]. It returns a 4-d array with a cross-spectral matrix for each input and in each frequency bin of the Fourier transform.

Parameters:
windowint, default=128

The length of the FFT window used for spectral estimation.

overlapfloat, default=0.75

The percentage of overlap between window.

fminfloat | None, default=None

The minimal frequency to be returned.

fmaxfloat | None, default=None

The maximal frequency to be returned.

fsfloat | None, default=None

The sampling frequency of the signal.

See also

CoSpectra
Coherences

Notes

New in version 0.6.

References

Attributes:
freqs_ndarray, shape (n_freqs,)

If transformed, the frequencies associated to cross-spectra. None if fs is None.

__init__(window=128, overlap=0.75, fmin=None, fmax=None, fs=None)

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:
selfCrossSpectra instance

The CrossSpectra 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 cross-spectral matrices.

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

Multi-channel time-series.

Returns:
X_newndarray, shape (n_matrices, n_channels, n_channels, n_freqs)

Cross-spectral matrices for each input and for each frequency bin.