pyriemann.estimation.XdawnCovariances¶
- class pyriemann.estimation.XdawnCovariances(nfilter=4, applyfilters=True, classes=None, estimator='scm', xdawn_estimator='scm', baseline_cov=None, **kwds)¶
Estimate special form covariance matrices for ERP combined with Xdawn.
Estimation of special form covariance matrix dedicated to ERP processing combined with Xdawn spatial filtering [1]. This is similar to
pyriemann.estimation.ERPCovariancesbut data are spatially filtered withpyriemann.spatialfilters.Xdawn.The advantage of this estimation is to reduce dimensionality of the covariance matrices supervisely.
- Parameters:
- nfilterint, default=4
Number of Xdawn filters per class.
- applyfiltersbool, default=True
If set to true, spatial filter are applied to the prototypes and the signals. When set to False, filters are applied only to the ERP prototypes allowing for a better generalization across subject and session at the expense of dimensionality increase. In that case, the estimation is similar to
pyriemann.estimation.ERPCovarianceswith svd=nfilter but with more compact prototype reduction.- classeslist of int | None, default=None
list of classes to take into account for prototype estimation. If None, all classes will be accounted.
- estimatorstring, default=”scm”
Covariance matrix estimator, see
pyriemann.utils.covariance.covariances().- xdawn_estimatorstring, default=”scm”
Covariance matrix estimator for Xdawn spatial filtering. Should be regularized using “lwf” or “oas”, see
pyriemann.utils.covariance.covariances().- baseline_covndarray, shape (n_channels, n_channels) | None, default=None
Baseline covariance for Xdawn spatial filtering, see
pyriemann.spatialfilters.Xdawn.- **kwdsdict
Any further parameters are passed directly to the covariance estimator.
- Attributes:
- P_ndarray, shape (n_classes x min(n_channels, n_filters), n_times)
If fit, the evoked response for each event type, concatenated.
See also
ERPCovariancesXdawn
References
[1]MEG decoding using Riemannian Geometry and Unsupervised classification A. Barachant. Technical report with the solution of the DecMeg 2014 challenge.
- __init__(nfilter=4, applyfilters=True, classes=None, estimator='scm', xdawn_estimator='scm', baseline_cov=None, **kwds)¶
Init.
- fit(X, y)¶
Fit.
Estimate spatial filters and prototyped response for each class.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- yndarray, shape (n_matrices,)
Labels for each matrix.
- Returns:
- selfXdawnCovariances instance
The XdawnCovariances instance.
- fit_transform(X, y)¶
Fit and transform in a single function.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- yndarray, shape (n_matrices,)
Labels for each matrix.
- Returns:
- X_newndarray, shape (n_matrices, n_components, n_components)
Covariance matrices filtered by Xdawn, where n_components is equal to 2 x n_classes x min(n_channels, nfilter) if applyfilters is True, and to n_channels + n_classes x min(n_channels, nfilter) otherwise.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequestencapsulating 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 Xdawn covariance matrices.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- Returns:
- X_newndarray, shape (n_matrices, n_components, n_components)
Covariance matrices filtered by Xdawn, where n_components is equal to 2 x n_classes x min(n_channels, nfilter) if applyfilters is True, and to n_channels + n_classes x min(n_channels, nfilter) otherwise.
