coffeine.covariance_transformers.Riemann

class coffeine.covariance_transformers.Riemann(metric: str = 'riemann', return_data_frame: bool = True)

Map SPD matrix to Riemannian tangent space.

Riemannian embedding step as described in [1]. Implements affine invariant metric, which makes assumption of full-rank inputs. The transform implies a log 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]

D. Sabbagh, P. Ablin, G. Varoquaux, A. Gramfort, and D.A. Engemann. Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. NeuroImage, page 116893,2020. ISSN 1053-8119. https://doi.org/10.1016/j.neuroimage.2020.116893