mne.decoding.EMS[source]¶Transformer to compute event-matched spatial filters.
This version of EMS [R36] operates on the entire time course. No time window needs to be specified. The result is a spatial filter at each time point and a corresponding time course. Intuitively, the result gives the similarity between the filter at each time point and the data vector (sensors) at that time point.
References
| [R36] | (1, 2) Aaron Schurger, Sebastien Marti, and Stanislas Dehaene, “Reducing multi-sensor data to a single time course that reveals experimental effects”, BMC Neuroscience 2013, 14:122 |
Attributes
filters_ |
(ndarray, shape (n_channels, n_times)) The set of spatial filters. |
classes_ |
(ndarray, shape (n_classes,)) The target classes. |
Methods
__hash__() <==> hash(x) |
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fit(X, y) |
Fit the spatial filters. |
fit_transform(X[, y]) |
Fit to data, then transform it. |
get_params() |
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set_params(**params) |
Set parameters (mimics sklearn API). |
transform(X) |
Transform the data by the spatial filters. |
__hash__() <==> hash(x)¶fit(X, y)[source]¶Fit the spatial filters.
| Parameters: | X : array, shape (n_epochs, n_channels, n_times)
y : array of int, shape (n_epochs)
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| Returns: | self : returns and instance of self. |
fit_transform(X, y=None, **fit_params)[source]¶Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
| Parameters: | X : numpy array of shape [n_samples, n_features]
y : numpy array of shape [n_samples]
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| Returns: | X_new : numpy array of shape [n_samples, n_features_new]
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