mne.decoding.UnsupervisedSpatialFilter

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

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

__hash__(/)	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.

fit(X, y=None)

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)

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

get_params(deep=True)

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)

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)

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)

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

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