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).
- estimatorinstance of
Methods
__hash__
(self, /)Return hash(self).
fit
(self, X[, y])Fit the spatial filters.
fit_transform
(self, X[, y])Transform the data to its filtered components after fitting.
get_params
(self[, deep])Get parameters for this estimator.
inverse_transform
(self, X)Inverse transform the data to its original space.
set_params
(self, \*\*params)Set the parameters of this estimator.
transform
(self, X)Transform the data to its spatial filters.
-
__hash__
(self, /)¶ Return hash(self).
-
fit
(self, X, y=None)[source]¶ Fit the spatial filters.
- Parameters
- Returns
- selfinstance of
UnsupervisedSpatialFilter
Return the modified instance.
- selfinstance of
-
fit_transform
(self, X, y=None)[source]¶ Transform the data to its filtered components after fitting.
-
set_params
(self, **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