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
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
¶

Visualization of SSVEP-based BCI Classification in Tangent Space

Classify fNIRS data with block diagonal matrices for HbO and HbR