pyriemann.estimation.CoSpectra¶

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

Estimation of co-spectral matrices.

Co-spectral matrices are SPD matrices estimated as the real part of the pyriemann.estimation.CrossSpectra. It returns a 4-d array with a co-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.

Attributes:

freqs_ndarray, shape (n_freqs,): If transformed, the frequencies associated to cospectra. None if fs is None.

See also

CrossSpectra
Coherences

__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 and transform in a single function.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_times): Multi-channel time-series.
yNone: Not used, here for compatibility with sklearn API.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels, n_freqs): Cross-spectral matrices for each input and for each frequency bin.

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 Introducing the set_output API for an example on how to use the API.

Parameters:

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

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

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: