pyriemann.spatialfilters.BilinearFilter¶

class pyriemann.spatialfilters.BilinearFilter(filters, log=False)¶

Bilinear spatial filter.

Bilinear spatial filter for SPD matrices allows to define a custom spatial filter for bilinear projection of the data:

\[\mathbf{Xf}_i = \mathbf{V} \mathbf{X}_i \mathbf{V}^T\]

If log parameter is set to true, will return the log of the diagonal:

\[\mathbf{xf}_i = \log ( \mathrm{diag} (\mathbf{Xf}_i) )\]

Parameters:

filtersndarray, shape (n_filters, n_channels): The filters for bilinear transform.
logbool, default=False: If true, return the log variance, otherwise return the spatially filtered covariance matrices.

Attributes:

filters_ndarray, shape (n_filters, n_channels): If fit, the filter components used to decompose the data for each event type, concatenated.

__init__(filters, log=False)¶: Init.

fit(X, y)¶

Train BilinearFilter spatial filters.

Parameters:

Xndarray, shape (n_trials, n_channels, n_channels): Set of covariance matrices.
yndarray, shape (n_trials,): Labels for each trial.

Returns:

selfBilinearFilter instance: The BilinearFilter 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)¶

Apply spatial filters.

Parameters:

Xndarray, shape (n_trials, n_channels, n_channels): Set of covariance matrices.

Returns:

Xfndarray, shape (n_trials, n_filters) or ndarray, shape (n_trials, n_filters, n_filters): Set of spatialy filtered log-variance or covariance, depending on the ‘log’ input parameter.