pyriemann.utils.mean.mean_covariance¶
- pyriemann.utils.mean.mean_covariance(covmats, metric='riemann', sample_weight=None, **kwargs)¶
Mean of matrices according to a metric.
Compute the mean of a set of matrices according to a metric [1].
- Parameters:
- covmatsndarray, shape (n_matrices, n, n)
Set of matrices.
- metricstring, default=’riemann’
The metric for mean, 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:
- Cndarray, shape (n, n)
Mean of matrices.
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