coffeine.covariance_transformers.RiemannSnp

class coffeine.covariance_transformers.RiemannSnp(rank='full', return_data_frame=True)

Map SPD matrix to Riemannian Wasserstein tangent space.

Riemannian Wasserstein embedding step as described in [1]. Implements Wasserstein metric that is not making a strong assumption of full-rank inputs. The transform implies a square-root non-linearity.

Parameters:

metricstr, default=’riemann’

The Riemannian metric to use. See PyRiemann documentation for details and valid choices.

return_data_framebool, default=True

Returning the result in a pandas data frame or not.

References

[1]

Sabbagh, D., Ablin, P., Varoquaux, G., Gramfort, A. and Engemann, D.A., 2019. Manifold-regression to predict from MEG/EEG brain signals without source modeling. Advances in Neural Information Processing Systems, 32.