This documentation is for development version 0.18.dev0.

mne.decoding.UnsupervisedSpatialFilter

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

Use unsupervised spatial filtering across time and samples.

Parameters:
estimator : instance of sklearn.base.BaseEstimator

Estimator using some decomposition algorithm.

average : bool, default False

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

Methods

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

Return hash(self).

fit(X, y=None)[source]

Fit the spatial filters.

Parameters:
X : array, shape (n_epochs, n_channels, n_times)

The data to be filtered.

y : None | array, shape (n_samples,)

Used for scikit-learn compatibility.

Returns:
self : instance of UnsupervisedSpatialFilter

Return the modified instance.

fit_transform(X, y=None)[source]

Transform the data to its filtered components after fitting.

Parameters:
X : array, shape (n_epochs, n_channels, n_times)

The data to be filtered.

y : None | array, shape (n_samples,)

Used for scikit-learn compatibility.

Returns:
X : array, shape (n_epochs, n_channels, n_times)

The transformed data.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

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

Returns:
params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(X)[source]

Inverse transform the data to its original space.

Parameters:
X : array, shape (n_epochs, n_components, n_times)

The data to be inverted.

Returns:
X : array, 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. Returns ——- self

transform(X)[source]

Transform the data to its spatial filters.

Parameters:
X : array, shape (n_epochs, n_channels, n_times)

The data to be filtered.

Returns:
X : array, shape (n_epochs, n_channels, n_times)

The transformed data.