API reference

Composing modeling pipelines

make_filter_bank_transformer(names[, ...])	Generate pipeline for filterbank models.
make_filter_bank_regressor(names[, method, ...])	Generate pipeline for regression with filter bank model.
make_filter_bank_classifier(names[, method, ...])	Generate pipeline for classification with filter bank model.

GaussianKernel([sigma])	Gaussian (squared exponential) Kernel.
KernelSum()	Sum multiple Kernels with fixed equal weights.

Computing Power-Spectral Features

get_frequency_bands([collection, subset])	Get pre-specified frequency bands based on the literature.
make_coffeine_data_frame(C[, names])	Put covariances in coffeine Data Frame.
compute_coffeine(inst[, frequencies, ...])	Compute & spectral features as SPD matrices in a Data Frame.
compute_features(inst[, features, duration, ...])	Compute features from raw data or clean epochs.

Covariance Transformers

NaiveVec(method[, return_data_frame])	Vectorize SPD matrix by flattening the upper triangle.
Diag([return_data_frame])	Vectorize SPD matrix by extracting diagonal.
LogDiag([return_data_frame])	Vectorize SPD matrix by extracting diagonal and computing the log.
Riemann([metric, return_data_frame])	Map SPD matrix to Riemannian tangent space.
RiemannSnp([rank, return_data_frame])	Map SPD matrix to Riemannian Wasserstein tangent space.
Snp(rank)	Map SPD matrix to Riemannian Wasserstein tangent space.
ExpandFeatures(estimator, expander_column)	Add binary interaction terms after projection step.

Spatiel Filters

ProjIdentitySpace()	Apply identy projection to SPD matrix.
ProjCommonSpace([scale, n_compo, reg])	Project SPD matrix to common subspace (PCA).
ProjLWSpace(shrink)	Apply regularization on covariance matrices.
ProjRandomSpace([n_compo, random_state])	Apply random projection to SPD matrix.
ProjSPoCSpace([shrink, scale, n_compo, reg])	Project SPD matrix subspace given by SPoC.