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Flexible-Extent ESMV (variant 2)

Solver ID: FLEX_ESMV2

Usage

from invert import Solver

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

solver = Solver("FLEX_ESMV2")
solver.make_inverse_operator(fwd)
stc = solver.apply_inverse_operator(evoked)
stc.plot()

Overview

Flex-extent ESMV wrapper around AdaptFlexESMV using a fixed set of diffusion smoothing orders (0/1/2).

References

  1. Lukas Hecker (2025). Unpublished.
  2. Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. (2014). Comparison of beamformers for EEG source signal reconstruction. Biomedical Signal Processing and Control, 14, 175-188.

API Reference

Bases: BaseSolver

Flex-extent beamformer tuned for the benchmark generator.

This is a thin wrapper around SolverAdaptFlexESMV with n_orders=2 (i.e., consider smoothing orders 0/1/2), matching the default synthetic multi_patch dataset which uses orders in [1, 2].

Source code in invert/solvers/beamformers/flex_esmv2.py 
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class SolverFlexESMV2(BaseSolver):
    """Flex-extent beamformer tuned for the benchmark generator.

    This is a thin wrapper around `SolverAdaptFlexESMV` with `n_orders=2`
    (i.e., consider smoothing orders 0/1/2), matching the default synthetic
    `multi_patch` dataset which uses orders in [1, 2].
    """

    meta = SolverMeta(
        slug="flex_esmv2",
        full_name="Flexible-Extent ESMV (variant 2)",
        category="Beamformers",
        description=(
            "Flex-extent ESMV wrapper around AdaptFlexESMV using a fixed set of "
            "diffusion smoothing orders (0/1/2)."
        ),
        references=[
            "Lukas Hecker (2025). Unpublished.",
            "Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. "
            "(2014). Comparison of beamformers for EEG source signal reconstruction. "
            "Biomedical Signal Processing and Control, 14, 175-188.",
        ],
    )

    def __init__(
        self,
        name: str = "FlexESMV2 (orders 0..2) Beamformer",
        reduce_rank: bool = True,
        rank: str | int = "auto",
        **kwargs,
    ):
        self.name = name
        # Keep kwargs for the inner solver, but avoid duplicating BaseSolver args
        self._inner_kwargs = dict(kwargs)
        self._inner_kwargs.pop("verbose", None)
        return super().__init__(reduce_rank=reduce_rank, rank=rank, **kwargs)

    def make_inverse_operator(self, forward, mne_obj, *args, alpha="auto", **kwargs):
        from .adapt_flex_esmv import SolverAdaptFlexESMV

        # Delegate to the tuned inner solver and expose its inverse operators
        self._inner = SolverAdaptFlexESMV(
            n_orders=2,
            diffusion_parameter=0.1,
            reduce_rank=self.reduce_rank,
            rank=self.rank,
            verbose=self.verbose,
            **self._inner_kwargs,
        )
        self._inner.make_inverse_operator(
            forward, mne_obj, *args, alpha=alpha, **kwargs
        )
        self.inverse_operators = self._inner.inverse_operators
        self.forward = self._inner.forward
        self.leadfield = self._inner.leadfield
        return self

    def apply_inverse_operator(self, mne_obj):  # type: ignore[override]
        return self._inner.apply_inverse_operator(mne_obj)

__init__ 

__init__(
    name: str = "FlexESMV2 (orders 0..2) Beamformer",
    reduce_rank: bool = True,
    rank: str | int = "auto",
    **kwargs,
)
Source code in invert/solvers/beamformers/flex_esmv2.py 
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def __init__(
    self,
    name: str = "FlexESMV2 (orders 0..2) Beamformer",
    reduce_rank: bool = True,
    rank: str | int = "auto",
    **kwargs,
):
    self.name = name
    # Keep kwargs for the inner solver, but avoid duplicating BaseSolver args
    self._inner_kwargs = dict(kwargs)
    self._inner_kwargs.pop("verbose", None)
    return super().__init__(reduce_rank=reduce_rank, rank=rank, **kwargs)

make_inverse_operator 

make_inverse_operator(
    forward, mne_obj, *args, alpha="auto", **kwargs
)
Source code in invert/solvers/beamformers/flex_esmv2.py 
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def make_inverse_operator(self, forward, mne_obj, *args, alpha="auto", **kwargs):
    from .adapt_flex_esmv import SolverAdaptFlexESMV

    # Delegate to the tuned inner solver and expose its inverse operators
    self._inner = SolverAdaptFlexESMV(
        n_orders=2,
        diffusion_parameter=0.1,
        reduce_rank=self.reduce_rank,
        rank=self.rank,
        verbose=self.verbose,
        **self._inner_kwargs,
    )
    self._inner.make_inverse_operator(
        forward, mne_obj, *args, alpha=alpha, **kwargs
    )
    self.inverse_operators = self._inner.inverse_operators
    self.forward = self._inner.forward
    self.leadfield = self._inner.leadfield
    return self

apply_inverse_operator 

apply_inverse_operator(mne_obj)
Source code in invert/solvers/beamformers/flex_esmv2.py 
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def apply_inverse_operator(self, mne_obj):  # type: ignore[override]
    return self._inner.apply_inverse_operator(mne_obj)