Modelling

In this module, we can find different method to model the relationship between stimulus and (EEG) response. Namely there are wrapper functions implementing:

  • Forward modelling (stimulus -> EEG), a.k.a _TRF_ (Temporal Response Functions)

  • Backward modelling (EEG -> stimulus)

  • CCA (in :mod:pyeeg.cca)

  • VAR model fitting

Updates: - 10/11/2023: added VAR model estimation (see fit_var() and fit_ar())

Classes

TRFEstimator([times, tmin, tmax, srate, ...])

Temporal Response Function (TRF) Estimator Class.

CCA_Estimator([times, tmin, tmax, ...])

Canonical Correlation (CCA) Estimator Class.

Functions

_svd_regress(x, y[, alpha, verbose])

Linear regression using svd.

fit_ar(x[, nlags, time_axis])

Fit an autoregressive model to a time series.

fit_var(x[, nlags, time_axis])

Fit a VAR model to a time series.