mne.decoding.EMS¶
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class
mne.decoding.
EMS
[source]¶ Transformer to compute event-matched spatial filters.
This version of EMS [R78ea2cca2302-1] 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
- R78ea2cca2302-1
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__
(self, /)Return hash(self).
fit
(self, X, y)Fit the spatial filters.
fit_transform
(self, X[, y])Fit to data, then transform it.
get_params
(self[, deep])Get the estimator params.
set_params
(self, \*\*params)Set parameters (mimics sklearn API).
transform
(self, X)Transform the data by the spatial filters.
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__hash__
(self, /)¶ Return hash(self).