moabb.pipelines.features.ExtendedSSVEPSignal

class moabb.pipelines.features.ExtendedSSVEPSignal[source][source]

Prepare FilterBank SSVEP EEG signal for estimating extended covariances

Riemannian approaches on SSVEP rely on extended covariances matrices, where the filtered signals are contenated to estimate a large covariance matrice.

FilterBank SSVEP EEG are of shape (n_trials, n_channels, n_times, n_freqs) and should be convert in (n_trials, n_channels*n_freqs, n_times) to estimate covariance matrices of (n_channels*n_freqs, n_channels*n_freqs).

Methods

fit(X, y)

No need to fit for ExtendedSSVEPSignal

fit_transform(X[, y])

Fit to data, then transform it.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Transpose and reshape EEG for extended covmat estimation

fit(X, y)[source][source]

No need to fit for ExtendedSSVEPSignal

transform(X)[source][source]

Transpose and reshape EEG for extended covmat estimation