pyriemann.utils.mean.mean_covariance¶

pyriemann.utils.mean.mean_covariance(X=None, metric='riemann', sample_weight=None, covmats=None, **kwargs)¶

Mean of matrices according to a metric.

Compute the mean of a set of matrices according to a metric [1].

Parameters:

Xndarray, shape (n_matrices, n, n): Set of matrices.
metricstring | callable, default=”riemann”: Metric for mean estimation, can be: “ale”, “alm”, “euclid”, “harmonic”, “identity”, “kullback_sym”, “logdet”, “logeuclid”, “riemann”, “wasserstein”, or a callable function.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weights for each matrix. If None, it uses equal weights.
**kwargsdict: The keyword arguments passed to the sub function.

Returns:

References

[1]

Review of Riemannian distances and divergences, applied to SSVEP-based BCI S. Chevallier, E. K. Kalunga, Q. Barthélemy, E. Monacelli. Neuroinformatics, Springer, 2021, 19 (1), pp.93-106