raw : instance of Raw
The raw data to decompose.
n_components : int | float | None
The number of components used for ICA decomposition. If int, it must be
smaller then max_pca_components. If None, all PCA components will be
used. If float between 0 and 1 components can will be selected by the
cumulative percentage of explained variance.
max_pca_components : int | None
The number of components used for PCA decomposition. If None, no
dimension reduction will be applied and max_pca_components will equal
the number of channels supplied on decomposing data.
n_pca_components
The number of PCA components used after ICA recomposition. The ensuing
attribute allows to balance noise reduction against potential loss of
features due to dimensionality reduction. If greater than
self.n_components_, the next 'n_pca_components' minus
'n_components_' PCA components will be added before restoring the
sensor space data. The attribute gets updated each time the according
parameter for in .pick_sources_raw or .pick_sources_epochs is changed.
noise_cov : None | instance of mne.cov.Covariance
Noise covariance used for whitening. If None, channels are just
z-scored.
random_state : None | int | instance of np.random.RandomState
np.random.RandomState to initialize the FastICA estimation.
As the estimation is non-deterministic it can be useful to
fix the seed to have reproducible results.
picks : array-like of int
Channels to be included. This selection remains throughout the
initialized ICA solution. If None only good data channels are used.
start : int | float | None
First sample to include for decomposition. If float, data will be
interpreted as time in seconds. If None, data will be used from the
first sample.
stop : int | float | None
Last sample to not include for decomposition. If float, data will be
interpreted as time in seconds. If None, data will be used to the
last sample.
start_find : int | float | None
First sample to include for artifact search. If float, data will be
interpreted as time in seconds. If None, data will be used from the
first sample.
stop_find : int | float | None
Last sample to not include for artifact search. If float, data will be
interpreted as time in seconds. If None, data will be used to the last
sample.
ecg_ch : str | ndarray | None
The target argument passed to ica.find_sources_raw. Either the
name of the ECG channel or the ECG time series. If None, this step
will be skipped.
ecg_score_func : str | callable
The score_func argument passed to ica.find_sources_raw. Either
the name of function supported by ICA or a custom function.
ecg_criterion : float | int | list-like | slice
The indices of the sorted skewness scores. If float, sources with
scores smaller than the criterion will be dropped. Else, the scores
sorted in descending order will be indexed accordingly.
E.g. range(2) would return the two sources with the highest score.
If None, this step will be skipped.
eog_ch : list | str | ndarray | None
The target argument or the list of target arguments subsequently
passed to ica.find_sources_raw. Either the name of the vertical EOG
channel or the corresponding EOG time series. If None, this step
will be skipped.
eog_score_func : str | callable
The score_func argument passed to ica.find_sources_raw. Either
the name of function supported by ICA or a custom function.
eog_criterion : float | int | list-like | slice
The indices of the sorted skewness scores. If float, sources with
scores smaller than the criterion will be dropped. Else, the scores
sorted in descending order will be indexed accordingly.
E.g. range(2) would return the two sources with the highest score.
If None, this step will be skipped.
skew_criterion : float | int | list-like | slice
The indices of the sorted skewness scores. If float, sources with
scores smaller than the criterion will be dropped. Else, the scores
sorted in descending order will be indexed accordingly.
E.g. range(2) would return the two sources with the highest score.
If None, this step will be skipped.
kurt_criterion : float | int | list-like | slice
The indices of the sorted skewness scores. If float, sources with
scores smaller than the criterion will be dropped. Else, the scores
sorted in descending order will be indexed accordingly.
E.g. range(2) would return the two sources with the highest score.
If None, this step will be skipped.
var_criterion : float | int | list-like | slice
The indices of the sorted skewness scores. If float, sources with
scores smaller than the criterion will be dropped. Else, the scores
sorted in descending order will be indexed accordingly.
E.g. range(2) would return the two sources with the highest score.
If None, this step will be skipped.
add_nodes : list of ica_nodes
Additional list if tuples carrying the following parameters:
(name : str, target : str | array, score_func : callable,
criterion : float | int | list-like | slice). This parameter is a
generalization of the artifact specific parameters above and has
the same structure. Example:
add_nodes=('ECG phase lock', ECG 01', my_phase_lock_function, 0.5)
verbose : bool, str, int, or None
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