pyeeg.cca.CCA_Estimator

class pyeeg.cca.CCA_Estimator(times=(0.0,), tmin=None, tmax=None, filterbank=False, freqs=(0.0,), srate=1.0, fit_intercept=True)

Canonical Correlation (CCA) Estimator Class.

xlags

Array of int, corresponding to lag in samples at which the TRF coefficients are computed

Type:

1d-array

times

Array of float, corresponding to lag in seconds at which the TRF coefficients are computed

Type:

1d-array

srate

Sampling rate

Type:

float

fit_intercept

Whether a column of ones should be added to the design matrix to fit an intercept

Type:

bool

intercept_

Intercepts

Type:

1d array (nchans, )

coef_

Actual TRF coefficients

Type:

ndarray (nlags, nfeats, nchans)

n_feats_

Number of word level features in TRF

Type:

int

n_chans_

Number of EEG channels in TRF

Type:

int

feat_names_

Names of each word level features

Type:

list

Notes

Attributes with a _ suffix are only set once the TRF has been fitted on EEG data

Methods

CCA_Estimator.fit(X, y[, ...])	Fit CCA model.
CCA_Estimator.get_metadata_routing()	Get metadata routing of this object.
CCA_Estimator.get_params([deep])	Get parameters for this estimator.
CCA_Estimator.plot_activation_map(pos[, ...])	Plot the activation map from the spatial filter.
CCA_Estimator.plot_all_dim_time([n_comp, n_dim])
CCA_Estimator.plot_compact_time([n_comp, dim])
CCA_Estimator.plot_corr(pos[, n_comp])	Plot the correlation between the EEG component waveform and the EEG channel waveform.
CCA_Estimator.plot_spatial_filter(pos[, n_comp])	Plot the topo of the feature requested.
CCA_Estimator.plot_time_filter([n_comp, dim])	Plot the TRF of the feature requested.
CCA_Estimator.set_fit_request(*[, ...])	Request metadata passed to the fit method.
CCA_Estimator.set_params(**params)	Set the parameters of this estimator.
CCA_Estimator.set_transform_request(*[, ...])	Request metadata passed to the transform method.
CCA_Estimator.transform([transform_x, ...])	Transform X and Y using the coefficients