Class Xdawn

mne/preprocessing/xdawn.py:215–533

Implementation of the Xdawn Algorithm. Xdawn :footcite:`RivetEtAl2009,RivetEtAl2011` is a spatial filtering method designed to improve the signal to signal + noise ratio (SSNR) of the ERP responses. Xdawn was originally designed for P300 evoked potential by enhancing the target resp

class Xdawn(XdawnTransformer):
    """Implementation of the Xdawn Algorithm.

    Xdawn :footcite:`RivetEtAl2009,RivetEtAl2011` is a spatial
    filtering method designed to improve the signal to signal + noise
    ratio (SSNR) of the ERP responses. Xdawn was originally designed for
    P300 evoked potential by enhancing the target response with respect
    to the non-target response. This implementation is a generalization
    to any type of ERP.

    Parameters
    ----------
    n_components : int, (default 2)
        The number of components to decompose the signals.
    signal_cov : None | Covariance | ndarray, shape (n_channels, n_channels)
        (default None). The signal covariance used for whitening of the data.
        if None, the covariance is estimated from the epochs signal.
    correct_overlap : 'auto' or bool (default 'auto')
        Compute the independent evoked responses per condition, while
        correcting for event overlaps if any. If 'auto', then
        overlapp_correction = True if the events do overlap.
    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 as ``method`` to
        :func:`mne.compute_covariance`.

    Attributes
    ----------
    filters_ : dict of ndarray
        If fit, the Xdawn components used to decompose the data for each event
        type, else empty. For each event type, the filters are in the rows of
        the corresponding array.
    patterns_ : dict of ndarray
        If fit, the Xdawn patterns used to restore the signals for each event
        type, else empty.
    evokeds_ : dict of Evoked
        If fit, the evoked response for each event type.
    event_id_ : dict
        The event id.
    correct_overlap_ : bool
        Whether overlap correction was applied.

    See Also
    --------
    mne.decoding.CSP, mne.decoding.SPoC

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

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

    def __init__(