pyriemann.estimation.Coherences

class pyriemann.estimation.Coherences(window=128, overlap=0.75, fmin=None, fmax=None, fs=None, coh='ordinary')[source]

Estimation of squared coherence matrices.

Squared coherence matrices estimation [1]. This method will return a 4-d array with a squared coherence matrix estimation for each input and in each frequency bin of the FFT.

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.

coh{“ordinary”, “instantaneous”, “lagged”, “imaginary”}, default=”ordinary”

Coherence type, see pyriemann.geometry.covariance.coherence().

Attributes:
freqs_ndarray, shape (n_freqs,)

If transformed, the frequencies associated to cospectra. None if fs is None.

See also

CrossSpectra
TimeDelayCovariances

Notes

Added in version 0.3.

References

__init__(window=128, overlap=0.75, fmin=None, fmax=None, fs=None, coh='ordinary')[source]

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:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

transform(X)[source]

Estimate the squared coherences 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)

Squared coherence matrices for each input and for each frequency bin.

Examples using pyriemann.estimation.Coherences

Ensemble learning on functional connectivity

Ensemble learning on functional connectivity