mne.decoding.UnsupervisedSpatialFilter(estimator, average=False)[source]¶Use unsupervised spatial filtering across time and samples.
| Parameters: | estimator : scikit-learn estimator
average : bool, defaults to False
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Methods
__hash__() <==> hash(x) |
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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. |
set_params(**params) |
Set the parameters of this estimator. |
transform(X) |
Transform the data to its spatial filters. |
__hash__() <==> hash(x)¶fit(X, y=None)[source]¶Fit the spatial filters.
| Parameters: | X : array, shape (n_epochs, n_channels, n_times)
y : None | array, shape (n_samples,)
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| Returns: | self : Instance of UnsupervisedSpatialFilter
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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)
y : None | array, shape (n_samples,)
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| Returns: | X : array, shape (n_trials, n_channels, n_times)
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get_params(deep=True)[source]¶Get parameters for this estimator.
| Parameters: | deep : boolean, optional
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| Returns: | params : mapping of string to any
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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