mne.decoding.UnsupervisedSpatialFilter#

class mne.decoding.UnsupervisedSpatialFilter(estimator, average=False)[source]#

Use unsupervised spatial filtering across time and samples.

Parameters:

estimatorinstance of sklearn.base.BaseEstimator

Estimator using some decomposition algorithm.

averagebool, default False

If True, the estimator is fitted on the average across samples (e.g. epochs).

Methods

fit(X[, y])	Fit the spatial filters.
fit_transform(X[, y])	Transform the data to its filtered components after fitting.
get_params([deep])	Get parameters for this estimator.
inverse_transform(X)	Inverse transform the data to its original space.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform the data to its spatial filters.

fit(X, y=None)[source]#

Fit the spatial filters.

Parameters:

Xarray, shape (n_epochs, n_channels, n_times)

The data to be filtered.

yNone | array, shape (n_samples,)

Used for scikit-learn compatibility.

Returns:

selfinstance of UnsupervisedSpatialFilter

Return the modified instance.

fit_transform(X, y=None)[source]#

Transform the data to its filtered components after fitting.

Parameters:

Xarray, shape (n_epochs, n_channels, n_times)

The data to be filtered.

yNone | array, shape (n_samples,)

Used for scikit-learn compatibility.

Returns:

Xarray, shape (n_epochs, n_channels, n_times)

The transformed data.

Examples using fit_transform:

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques

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.

inverse_transform(X)[source]#

Inverse transform the data to its original space.

Parameters:

Xarray, shape (n_epochs, n_components, n_times)

The data to be inverted.

Returns:

Xarray, shape (n_epochs, n_channels, n_times)

The transformed data.

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.

transform(X)[source]#

Transform the data to its spatial filters.

Parameters:

Xarray, shape (n_epochs, n_channels, n_times)

The data to be filtered.

Returns:

Xarray, shape (n_epochs, n_channels, n_times)

The transformed data.

Examples using mne.decoding.UnsupervisedSpatialFilter#

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques