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EPIFOCUS

Solver ID: EPIFOCUS

Usage

from invert import Solver

# fwd = ...    (mne.Forward object)
# evoked = ... (mne.Evoked object)

solver = Solver("EPIFOCUS")
solver.make_inverse_operator(fwd)
stc = solver.apply_inverse_operator(evoked)
stc.plot()

Overview

Epileptic focus localization method that uses source-wise normalization/weighting to promote focal solutions.

References

  1. Grave de Peralta Menendez, R., Gonzalez Andino, S., Lantz, G., Michel, C. M., & Landis, T. (2001). Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations. Brain Topography, 14(2), 131–137.

API Reference

Bases: BaseSolver

Class for the EPIFOCUS inverse solution [1].

References

[1] Menendez, R. G. D. P., Andino, S. G., Lantz, G., Michel, C. M., & Landis, T. (2001). Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations. Brain topography, 14(2), 131-137.

Source code in invert/solvers/minimum_norm/epifocus.py 
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class SolverEPIFOCUS(BaseSolver):
    """Class for the EPIFOCUS inverse solution [1].

    References
    ----------
    [1] Menendez, R. G. D. P., Andino, S. G., Lantz, G., Michel, C. M., &
    Landis, T. (2001). Noninvasive localization of electromagnetic epileptic
    activity. I. Method descriptions and simulations. Brain topography, 14(2),
    131-137.

    """

    meta = SolverMeta(
        acronym="EPIFOCUS",
        full_name="EPIFOCUS",
        category="Minimum Norm",
        description=(
            "Epileptic focus localization method that uses source-wise "
            "normalization/weighting to promote focal solutions."
        ),
        references=[
            "Grave de Peralta Menendez, R., Gonzalez Andino, S., Lantz, G., Michel, C. M., & Landis, T. (2001). Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations. Brain Topography, 14(2), 131–137.",
        ],
    )

    def __init__(self, name="EPIFOCUS", **kwargs):
        self.name = name
        return super().__init__(**kwargs)

    def make_inverse_operator(self, forward, *args, alpha="auto", **kwargs):
        """Calculate inverse operator.

        Parameters
        ----------
        forward : mne.Forward
            The mne-python Forward model instance.
        alpha : float
            The regularization parameter.

        Return
        ------
        self : object returns itself for convenience
        """
        super().make_inverse_operator(forward, *args, alpha=alpha, **kwargs)
        leadfield = self.leadfield
        leadfield -= leadfield.mean(axis=0)

        n_chans, _ = leadfield.shape

        W = np.diag(1 / np.linalg.norm(leadfield, axis=0))
        T = leadfield @ W
        inverse_operator = np.array([np.linalg.pinv(Ti[:, np.newaxis]) for Ti in T.T])[
            :, 0
        ]
        inverse_operators = [
            inverse_operator,
        ]

        self.inverse_operators = [
            InverseOperator(inverse_operator, self.name)
            for inverse_operator in inverse_operators
        ]
        return self

__init__ 

__init__(name='EPIFOCUS', **kwargs)
Source code in invert/solvers/minimum_norm/epifocus.py 
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def __init__(self, name="EPIFOCUS", **kwargs):
    self.name = name
    return super().__init__(**kwargs)

make_inverse_operator 

make_inverse_operator(
    forward, *args, alpha="auto", **kwargs
)

Calculate inverse operator.

Parameters:

Name Type Description Default
forward Forward

The mne-python Forward model instance.

 required
alpha float

The regularization parameter.

 'auto'
Return

self : object returns itself for convenience

Source code in invert/solvers/minimum_norm/epifocus.py 
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def make_inverse_operator(self, forward, *args, alpha="auto", **kwargs):
    """Calculate inverse operator.

    Parameters
    ----------
    forward : mne.Forward
        The mne-python Forward model instance.
    alpha : float
        The regularization parameter.

    Return
    ------
    self : object returns itself for convenience
    """
    super().make_inverse_operator(forward, *args, alpha=alpha, **kwargs)
    leadfield = self.leadfield
    leadfield -= leadfield.mean(axis=0)

    n_chans, _ = leadfield.shape

    W = np.diag(1 / np.linalg.norm(leadfield, axis=0))
    T = leadfield @ W
    inverse_operator = np.array([np.linalg.pinv(Ti[:, np.newaxis]) for Ti in T.T])[
        :, 0
    ]
    inverse_operators = [
        inverse_operator,
    ]

    self.inverse_operators = [
        InverseOperator(inverse_operator, self.name)
        for inverse_operator in inverse_operators
    ]
    return self