pyriemann.tangentspace.FGDA

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

Fisher Geodesic Discriminant analysis.

Project data in Tangent space, apply a FLDA to reduce dimention, and project filtered data back in the manifold. For a complete description of the algorithm, see [1].

Parameters
metricstring | dict, default=’riemann’

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

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.

See also

FgMDM
TangentSpace

References

1

Riemannian geometry applied to BCI classification A. Barachant, S. Bonnet, M. Congedo and C. Jutten. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), LNCS vol. 6365, 2010, p. 629-636.

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 point and the FLDA.

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
selfFGDA instance

The FGDA 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
covsndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices after filtering.

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_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)

Filtering operation.

Parameters
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns
covsndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices after filtering.