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. This is similar to
pyriemann.estimation.ERPCovariancesbut data are spatially filtered withpyriemann.spatialfilters.Xdawn. A complete description of the method is available in [1].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_covarray, 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 classes.
- 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=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
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:
- covmatsndarray, 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.
