Function gamma_map

mne/inverse_sparse/_gamma_map.py:181–341

Hierarchical Bayes (Gamma-MAP) sparse source localization method. Models each source time course using a zero-mean Gaussian prior with an unknown variance (gamma) parameter. During estimation, most gammas are driven to zero, resulting in a sparse source estimate, as in :footcite:`Wi

(
    evoked,
    forward,
    noise_cov,
    alpha,
    loose="auto",
    depth=0.8,
    xyz_same_gamma=True,
    maxit=10000,
    tol=1e-6,
    update_mode=1,
    gammas=None,
    pca=True,
    return_residual=False,
    return_as_dipoles=False,
    rank=None,
    pick_ori=None,
    verbose=None,
)

@verbose
def gamma_map(
    evoked,
    forward,
    noise_cov,
    alpha,
    loose="auto",
    depth=0.8,
    xyz_same_gamma=True,
    maxit=10000,
    tol=1e-6,
    update_mode=1,
    gammas=None,
    pca=True,
    return_residual=False,
    return_as_dipoles=False,
    rank=None,
    pick_ori=None,
    verbose=None,
):
    """Hierarchical Bayes (Gamma-MAP) sparse source localization method.

    Models each source time course using a zero-mean Gaussian prior with an
    unknown variance (gamma) parameter. During estimation, most gammas are
    driven to zero, resulting in a sparse source estimate, as in
    :footcite:`WipfEtAl2007` and :footcite:`WipfNagarajan2009`.

    For fixed-orientation forward operators, a separate gamma is used for each
    source time course, while for free-orientation forward operators, the same
    gamma is used for the three source time courses at each source space point
    (separate gammas can be used in this case by using xyz_same_gamma=False).

    Parameters
    ----------
    evoked : instance of Evoked
        Evoked data to invert.
    forward : dict
        Forward operator.
    noise_cov : instance of Covariance
        Noise covariance to compute whitener.
    alpha : float
        Regularization parameter (noise variance).
    %(loose)s
    %(depth)s
    xyz_same_gamma : bool
        Use same gamma for xyz current components at each source space point.
        Recommended for free-orientation forward solutions.
    maxit : int
        Maximum number of iterations.
    tol : float
        Tolerance parameter for convergence.
    update_mode : int
        Update mode, 1: MacKay update (default), 2: Modified MacKay update.
    gammas : array, shape=(n_sources,)
        Initial values for posterior variances (gammas). If None, a
        variance of 1.0 is used.
    pca : bool
        If True the rank of the data is reduced to the true dimension.
    return_residual : bool