pyavs.compute_ica

pyavs.compute_ica(raw: mne.io.Raw, n_components: int | None = None, method: str = 'fastica', fit_params: dict | None = None, max_iter: int = 200, random_state: int = 42, picks: str | list | None = 'meg', decim: int | None = None, reject: dict | None = False, reject_by_annotation: bool = True, verbose: bool = True) → mne.preprocessing.ICA[source]

Compute ICA decomposition on MEG data.

Parameters:

  • raw (mne.io.Raw) – MEG raw data.

  • n_components (int, optional) – Number of ICA components (default: None, uses min(80, n_meg_channels)).

  • method (str, optional) – ICA algorithm (default: ‘infomax’).

  • fit_params (dict, optional) – Additional parameters for ICA fitting.

  • max_iter (int, optional) – Maximum number of iterations (default: 200).

  • random_state (int, optional) – Random seed for reproducibility (default: 42).

  • picks (str or list, optional) – Channels to include (default: ‘meg’).

  • decim (int, optional) – Decimation factor (default: None).

  • reject (dict, optional) – Rejection criteria for fitting.

  • reject_by_annotation (bool, optional) – Whether to reject by annotations (default: True).

  • verbose (bool, optional) – Whether to log progress.

Returns:

Fitted ICA object.

Return type:

mne.preprocessing.ICA