mne.decoding.TimeDelayingRidge#

class mne.decoding.TimeDelayingRidge(tmin, tmax, sfreq, alpha=0.0, reg_type='ridge', fit_intercept=True, n_jobs=None, edge_correction=True)[source]#

Ridge regression of data with time delays.

Parameters:

tminint | float

The starting lag, in seconds (or samples if sfreq == 1). Negative values correspond to times in the past.

tmaxint | float

The ending lag, in seconds (or samples if sfreq == 1). Positive values correspond to times in the future. Must be >= tmin.

sfreqfloat

The sampling frequency used to convert times into samples.

alphafloat

The ridge (or laplacian) regularization factor.

reg_typestr | list

Can be “ridge” (default) or “laplacian”. Can also be a 2-element list specifying how to regularize in time and across adjacent features.

fit_interceptbool

If True (default), the sample mean is removed before fitting.

n_jobsint | str

The number of jobs to use. Can be an int (default 1) or 'cuda'.

New in version 0.18.

edge_correctionbool

If True (default), correct the autocorrelation coefficients for non-zero delays for the fact that fewer samples are available. Disabling this speeds up performance at the cost of accuracy depending on the relationship between epoch length and model duration. Only used if estimator is float or None.

New in version 0.18.

See also

mne.decoding.ReceptiveField

Notes

This class is meant to be used with mne.decoding.ReceptiveField by only implicitly doing the time delaying. For reasonable receptive field and input signal sizes, it should be more CPU and memory efficient by using frequency-domain methods (FFTs) to compute the auto- and cross-correlations.

Methods

fit(X, y)	Estimate the coefficients of the linear model.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict the output.
set_params(**params)	Set the parameters of this estimator.

fit(X, y)[source]#

Estimate the coefficients of the linear model.

Parameters:

Xarray, shape (n_samples[, n_epochs], n_features)

The training input samples to estimate the linear coefficients.

yarray, shape (n_samples[, n_epochs], n_outputs)

The target values.

Returns:

selfinstance of TimeDelayingRidge

Returns the modified instance.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

predict(X)[source]#

Predict the output.

Parameters:

Xarray, shape (n_samples[, n_epochs], n_features)

The data.

Returns:

Xndarray

The predicted response.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Parameters.

Returns:

instinstance

The object.

Examples using mne.decoding.TimeDelayingRidge#

Spectro-temporal receptive field (STRF) estimation on continuous data

Spectro-temporal receptive field (STRF) estimation on continuous data