Class ICA

mne/preprocessing/ica.py:200–2688

Data decomposition using Independent Component Analysis (ICA). This object estimates independent components from :class:`mne.io.Raw`, :class:`mne.Epochs`, or :class:`mne.Evoked` objects. Components can optionally be removed (for artifact repair) prior to signal reconstruction. .. w

@fill_doc
class ICA(ContainsMixin):
    """Data decomposition using Independent Component Analysis (ICA).

    This object estimates independent components from :class:`mne.io.Raw`,
    :class:`mne.Epochs`, or :class:`mne.Evoked` objects. Components can
    optionally be removed (for artifact repair) prior to signal reconstruction.

    .. warning:: ICA is sensitive to low-frequency drifts and therefore
                 requires the data to be high-pass filtered prior to fitting.
                 Typically, a cutoff frequency of 1 Hz is recommended.

    Parameters
    ----------
    n_components : int | float | None
        Number of principal components (from the pre-whitening PCA step) that
        are passed to the ICA algorithm during fitting:

        - :class:`int`
            Must be greater than 1 and less than or equal to the number of
            channels.
        - :class:`float` between 0 and 1 (exclusive)
            Will select the smallest number of components required to explain
            the cumulative variance of the data greater than ``n_components``.
            Consider this hypothetical example: we have 3 components, the first
            explaining 70%%, the second 20%%, and the third the remaining 10%%
            of the variance. Passing 0.8 here (corresponding to 80%% of
            explained variance) would yield the first two components,
            explaining 90%% of the variance: only by using both components the
            requested threshold of 80%% explained variance can be exceeded. The
            third component, on the other hand, would be excluded.
        - ``None``
            ``0.999999`` will be used. This is done to avoid numerical
            stability problems when whitening, particularly when working with
            rank-deficient data.

        Defaults to ``None``. The actual number used when executing the
        :meth:`ICA.fit` method will be stored in the attribute
        ``n_components_`` (note the trailing underscore).

        .. versionchanged:: 0.22
           For a :class:`python:float`, the number of components will account
           for *greater than* the given variance level instead of *less than or
           equal to* it. The default (None) will also take into account the
           rank deficiency of the data.
    noise_cov : None | instance of Covariance
        Noise covariance used for pre-whitening. If None (default), channels
        are scaled to unit variance ("z-standardized") as a group by channel
        type prior to the whitening by PCA.
    %(random_state)s
    method : 'fastica' | 'infomax' | 'picard'
        The ICA method to use in the fit method. Use the ``fit_params`` argument
        to set additional parameters. Specifically, if you want Extended
        Infomax, set ``method='infomax'`` and ``fit_params=dict(extended=True)``
        (this also works for ``method='picard'``). Defaults to ``'fastica'``.
        For reference, see :footcite:`Hyvarinen1999,BellSejnowski1995,LeeEtAl1999,AblinEtAl2018`.
    fit_params : dict | None
        Additional parameters passed to the ICA estimator as specified by
        ``method``. Allowed entries are determined by the various algorithm