mne.decoding.EMS#
- class mne.decoding.EMS[source]#
Transformer to compute event-matched spatial filters.
This version of EMS [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.
Note
EMS only works for binary classification.
References
- Attributes:
Methods
fit
(X, y)Fit the spatial filters.
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])Get the estimator params.
set_params
(**params)Set parameters (mimics sklearn API).
transform
(X)Transform the data by the spatial filters.
- fit(X, y)[source]#
Fit the spatial filters.
- Parameters:
- Returns:
- selfinstance of
EMS
Returns self.
- selfinstance of
Examples using
fit
:Compute effect-matched-spatial filtering (EMS)
Compute effect-matched-spatial filtering (EMS)
- fit_transform(X, y=None, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
, and returns a transformed version ofX
.
- set_params(**params)[source]#
Set parameters (mimics sklearn API).
- Parameters:
- **params
dict
Extra parameters.
- **params
- Returns:
- instobject
The instance.
- transform(X)[source]#
Transform the data by the spatial filters.
- Parameters:
- X
array
, shape (n_epochs, n_channels, n_times) The input data.
- X
- Returns:
- X
array
, shape (n_epochs, n_times) The input data transformed by the spatial filters.
- X
Examples using
transform
:Compute effect-matched-spatial filtering (EMS)
Compute effect-matched-spatial filtering (EMS)
Examples using mne.decoding.EMS
#
Compute effect-matched-spatial filtering (EMS)