pyriemann.estimation.BlockCovariances¶

class pyriemann.estimation.BlockCovariances(block_size, estimator='scm', **kwds)¶

Estimation of block covariance matrices.

Perform a block covariance estimation for each given matrix. The resulting matrices are block diagonal matrices.

The blocks on the diagonal are calculated as individual covariance matrices for a subset of channels using the given the estimator. Varying block sized possible by passing a list to allow incorporation of different modalities with different number of channels (e.g. EEG, ECoG, LFP, EMG) with their own respective covariance matrices.

Parameters:

block_sizeint | array-like of int: Sizes of individual blocks given as int for same-size block, or list for varying block sizes.
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

Notes

Added in version 0.3.

__init__(block_size, 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:

selfCovariances instance: The Covariances 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): Covariance matrices.

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)¶

Estimate block covariance matrices.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_times): Multi-channel time-series.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels): Covariance matrices.

Examples using `pyriemann.estimation.BlockCovariances`¶

Offline SSVEP-based BCI Multiclass Prediction

Offline SSVEP-based BCI Multiclass Prediction

Visualization of SSVEP-based BCI Classification in Tangent Space

Visualization of SSVEP-based BCI Classification in Tangent Space

Classify fNIRS data with block diagonal matrices for HbO and HbR

Classify fNIRS data with block diagonal matrices for HbO and HbR