mne.decoding.LinearModel#

class mne.decoding.LinearModel(model=None)[source]#

Compute and store patterns from linear models.

The linear model coefficients (filters) are used to extract discriminant neural sources from the measured data. This class computes the corresponding patterns of these linear filters to make them more interpretable 1.

Parameters

modelobject | None: A linear model from scikit-learn with a fit method that updates a coef_ attribute. If None the model will be LogisticRegression.

See also

CSP
mne.preprocessing.ICA
mne.preprocessing.Xdawn

Notes

New in v0.10.

References

1: Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, John-Dylan Haynes, Benjamin Blankertz, and Felix Bießmann. On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87:96–110, 2014. doi:10.1016/j.neuroimage.2013.10.067.

Attributes

filters_ndarray, shape ([n_targets], n_features): If fit, the filters used to decompose the data.
patterns_ndarray, shape ([n_targets], n_features): If fit, the patterns used to restore M/EEG signals.

Methods

`fit`(X, y, **fit_params)	Estimate the coefficients of the linear model.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y, **fit_params)[source]#

Estimate the coefficients of the linear model.

Save the coefficients in the attribute filters_ and computes the attribute patterns_.

Parameters

Xarray, shape (n_samples, n_features): The training input samples to estimate the linear coefficients.
yarray, shape (n_samples, [n_targets]): The target values.
**fit_paramsdict of str -> object: Parameters to pass to the fit method of the estimator.

Returns

selfinstance of LinearModel: Returns the modified instance.

Examples using fit:

Linear classifier on sensor data with plot patterns and filters

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters

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

Returns

paramsdict: Parameter names mapped to their values.

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.

Parameters