Class LinearModel

mne/decoding/base.py:416–572

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 :footcite:`HaufeEtAl2

class LinearModel(MetaEstimatorMixin, BaseEstimator):
    """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 :footcite:`HaufeEtAl2014`.

    Parameters
    ----------
    model : object | None
        A linear model from scikit-learn with a fit method
        that updates a ``coef_`` attribute.
        If None the model will be
        :class:`sklearn.linear_model.LogisticRegression`.

    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.

    See Also
    --------
    CSP
    mne.preprocessing.ICA
    mne.preprocessing.Xdawn

    Notes
    -----
    .. versionadded:: 0.10

    References
    ----------
    .. footbibliography::
    """

    _model_attr_wrap = (
        "transform",
        "fit_transform",
        "predict",
        "predict_proba",
        "predict_log_proba",
        "_estimator_type",  # remove after sklearn 1.6
        "decision_function",
        "score",
        "classes_",
    )

    def __init__(self, model=None):
        self.model = model

    def __sklearn_tags__(self):
        """Get sklearn tags."""
        tags = super().__sklearn_tags__()
        model = self.model if self.model is not None else LogisticRegression()
        model_tags = model.__sklearn_tags__()