pyriemann.tangentspace.TangentSpace

class pyriemann.tangentspace.TangentSpace(metric='riemann', tsupdate=False)

Tangent space projection.

Tangent space projection maps a set of SPD matrices to their tangent space according to [1]. The tangent space projection can be seen as a kernel operation, cf [2]. After projection, each matrix is represented as a vector of size \(n (n+1)/2\), where \(n\) is the dimension of the SPD matrices.

Tangent space projection is useful to convert SPD matrices in Euclidean vectors while conserving the inner structure of the manifold. After projection, standard processing and vector-based classification can be applied.

Tangent space projection is a local approximation of the manifold. It takes one parameter, the reference matrix, that is usually estimated using the geometric mean of the SPD matrices set you project. If the function fit is not called, the identity matrix will be used as reference matrix. This can lead to serious degradation of performances. The approximation will be bigger if the matrices in the set are scattered in the manifold, and lower if they are grouped in a small region of the manifold.

After projection, it is possible to go back in the manifold using the inverse transform.

Parameters:

metricstring | dict, default=”riemann”

The type of metric used for reference matrix estimation (for the list of supported metrics see pyriemann.utils.mean.mean_covariance()) and for tangent space map (see pyriemann.utils.tangent_space.tangent_space()). The metric can be a dict with two keys, “mean” and “map” in order to pass different metrics.

tsupdatebool, default=False

Activate tangent space update for covariante shift correction between training and test, as described in [2]. This is not compatible with online implementation. Performance are better when the number of matrices for prediction is higher.

Attributes:

reference_ndarray, shape (n_channels, n_channels)

If fit, the reference matrix for tangent space mapping.

See also

FgMDM

FGDA

References

[1]

Multiclass Brain-Computer Interface Classification by Riemannian Geometry A. Barachant, S. Bonnet, M. Congedo, and C. Jutten. IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012.

[2] (1,2)

Classification of covariance matrices using a Riemannian-based kernel for BCI applications A. Barachant, S. Bonnet, M. Congedo and C. Jutten. Neurocomputing, Elsevier, 2013, 112, pp.172-178.

__init__(metric='riemann', tsupdate=False)

Init.

fit(X, y=None, sample_weight=None)

Fit (estimates) the reference matrix.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

yNone

Not used, here for compatibility with sklearn API.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

Returns:

selfTangentSpace instance

The TangentSpace instance.

fit_transform(X, y=None, sample_weight=None)

Fit and transform in a single function.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

yNone

Not used, here for compatibility with sklearn API.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

Returns:

tsndarray, shape (n_matrices, n_ts)

Tangent space projections of SPD 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.

inverse_transform(X)

Inverse transform.

Project back a set of tangent space vector in the manifold.

Parameters:

Xndarray, shape (n_matrices, n_ts)

Set of tangent space projections of the matrices.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices corresponding to each of tangent vector.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TangentSpace

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

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)

Tangent space projection.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns:

tsndarray, shape (n_matrices, n_ts)

Tangent space projections of SPD matrices.

Examples using pyriemann.tangentspace.TangentSpace

ERP EEG decoding in Tangent space.

ERP EEG decoding in Tangent space.

Augmented Covariance Matrix

Augmented Covariance Matrix

Ensemble learning on functional connectivity

Ensemble learning on functional connectivity

Visualization of SSVEP-based BCI Classification in Tangent Space

Visualization of SSVEP-based BCI Classification in Tangent Space

Manova for ERP data

Manova for ERP data

Comparison of pipelines for transfer learning

Comparison of pipelines for transfer learning