mne.decoding.EMS

class mne.decoding.EMS

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.

Attributes:

filters_ndarray, shape (n_channels, n_times)

The set of spatial filters.

classes_ndarray, shape (n_classes,)

The target classes.

Methods

fit(X, y)	Fit the spatial filters.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform the data by the spatial filters.

References

[1]

Aaron Schurger, Sebastien Marti, and Stanislas Dehaene. Reducing multi-sensor data to a single time course that reveals experimental effects. BMC Neuroscience, 2013. doi:10.1186/1471-2202-14-122.

fit(X, y)

Fit the spatial filters.

Parameters:

Xarray, shape (n_epochs, n_channels, n_times)

The training data.

yarray of int, shape (n_epochs)

The target classes.

Returns:

selfinstance of EMS

Returns self.

fit_transform(X, y=None, **fit_params)

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:

Xarray_like of shape (n_samples, n_features)

Input samples.

yarray_like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• “polars”: Polars output

• None: Transform configuration is unchanged

New in v1.4: “polars” option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X)

Transform the data by the spatial filters.

Parameters:

Xarray, shape (n_epochs, n_channels, n_times)

The input data.

Returns:

Xarray, shape (n_epochs, n_times)

The input data transformed by the spatial filters.

Examples using mne.decoding.EMS

Decoding (MVPA)

Decoding (MVPA)

Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)