pyeeg.mcca.mCCA
- class pyeeg.mcca.mCCA(n_components=None)
Class to support mCCA computation on a set of data matrices. Typically N data matrices, with SAME number of samples (observations) but possibly different number of channels (features). A typical use case would be to find common source of activity within each matrix, for instance where they carry EEG data for individual subject, and one wants to denoise the EEG data by projecting each subject’s EEG into a space where they share a common response. The projection matrices will be per individual.
- Parameters:
ncomponents (int) – number of compoenents to keep
- n_components
- Type:
int
- mixing
- Type:
list
- fit()
- canonical_correlate_single()
- denoise()
References
De Cheveigné et. al, MCCA of brain signals, 2018, biorXiv
Methods
mCCA.canonical_correlate_single(X, idx)Project one single dataset into its canonical correlate components.
mCCA.denoise(X, num_comps, idx)
mCCA.fit(X)Get metadata routing of this object.
mCCA.get_params([deep])Get parameters for this estimator.
mCCA.set_params(**params)Set the parameters of this estimator.