Class SPoC

mne/decoding/csp.py:680–911

Implementation of the SPoC spatial filtering. Source Power Comodulation (SPoC) :footcite:`DahneEtAl2014` allows to extract spatial filters and patterns by using a target (continuous) variable in the decomposition process in order to give preference to components whose power correlat

@fill_doc
class SPoC(CSP):
    """Implementation of the SPoC spatial filtering.

    Source Power Comodulation (SPoC) :footcite:`DahneEtAl2014` allows to
    extract spatial filters and
    patterns by using a target (continuous) variable in the decomposition
    process in order to give preference to components whose power correlates
    with the target variable.

    SPoC can be seen as an extension of the CSP driven by a continuous
    variable rather than a discrete variable. Typical applications include
    extraction of motor patterns using EMG power or audio patterns using sound
    envelope.

    Parameters
    ----------
    n_components : int
        The number of components to decompose M/EEG signals.
    reg : float | str | None (default None)
        If not None (same as ``'empirical'``, default), allow
        regularization for covariance estimation.
        If float, shrinkage is used (0 <= shrinkage <= 1).
        For str options, ``reg`` will be passed to ``method`` to
        :func:`mne.compute_covariance`.
    log : None | bool (default None)
        If transform_into == 'average_power' and log is None or True, then
        applies a log transform to standardize the features, else the features
        are z-scored. If transform_into == 'csp_space', then log must be None.
    transform_into : {'average_power', 'csp_space'}
        If 'average_power' then self.transform will return the average power of
        each spatial filter. If 'csp_space' self.transform will return the data
        in CSP space. Defaults to 'average_power'.
    cov_method_params : dict | None
        Parameters to pass to :func:`mne.compute_covariance`.

        .. versionadded:: 0.16
    restr_type : "restricting" | "whitening" | None
        Restricting transformation for covariance matrices before performing
        generalized eigendecomposition.
        If "restricting" only restriction to the principal subspace of signal_cov
        will be performed.
        If "whitening", covariance matrices will be additionally rescaled according
        to the whitening for the signal_cov.
        If None, no restriction will be applied. Defaults to None.

        .. versionadded:: 1.11
    info : mne.Info | None
        The mne.Info object with information about the sensors and methods of
        measurement used for covariance estimation and generalized
        eigendecomposition.
        If None, one channel type and no projections will be assumed and if
        rank is dict, it will be sum of ranks per channel type.
        Defaults to None.

        .. versionadded:: 1.11
    %(rank_none)s

        .. versionadded:: 0.17