Parameters:  epochs : instance of Epochs, or a list of Epochs objects
keep_sample_mean : bool (default true)
If False, the average response over epochs is computed for
each event type and subtracted during the covariance
computation. This is useful if the evoked response from a
previous stimulus extends into the baseline period of the next.
Note. This option is only implemented for method=’empirical’.
tmin : float  None (default None)
Start time for baseline. If None start at first sample.
tmax : float  None (default None)
End time for baseline. If None end at last sample.
projs : list of Projection  None (default None)
List of projectors to use in covariance calculation, or None
to indicate that the projectors from the epochs should be
inherited. If None, then projectors from all epochs must match.
method : str  list  None (default ‘empirical’)
The method used for covariance estimation. If ‘empirical’ (default),
the sample covariance will be computed. A list can be passed to run a
set of the different methods.
If ‘auto’ or a list of methods, the best estimator will be determined
based on loglikelihood and crossvalidation on unseen data as
described in ref. [1]. Valid methods are:
‘empirical’, the empirical or sample covariance,
‘diagonal_fixed’, a diagonal regularization as in mne.cov.regularize
(see MNE manual), ‘ledoit_wolf’, the LedoitWolf estimator (see [2]),
‘shrunk’ like ‘ledoit_wolf’ with crossvalidation for optimal alpha
(see scikitlearn documentation on covariance estimation), ‘pca’,
probabilistic PCA with low rank
(see [3]), and, ‘factor_analysis’, Factor Analysis with low rank
(see [4]). If ‘auto’, expands to:
['shrunk', 'diagonal_fixed', 'empirical', 'factor_analysis']
Note. ‘ledoit_wolf’ and ‘pca’ are similar to ‘shrunk’ and
‘factor_analysis’, respectively. They are not included to avoid
redundancy. In most cases ‘shrunk’ and ‘factor_analysis’ represent
more appropriate default choices.
method_params : dict  None (default None)
Additional parameters to the estimation procedure. Only considered if
method is not None. Keys must correspond to the value(s) of method.
If None (default), expands to:
'empirical': {'store_precision': False, 'assume_centered': True},
'diagonal_fixed': {'grad': 0.01, 'mag': 0.01, 'eeg': 0.0,
'store_precision': False,
'assume_centered': True},
'ledoit_wolf': {'store_precision': False, 'assume_centered': True},
'shrunk': {'shrinkage': np.logspace(4, 0, 30),
'store_precision': False, 'assume_centered': True},
'pca': {'iter_n_components': None},
'factor_analysis': {'iter_n_components': None}
cv : int  sklearn cross_validation object (default 3)
The cross validation method. Defaults to 3, which will
internally trigger a default 3fold shuffle split.
scalings : dict  None (default None)
Defaults to dict(mag=1e15, grad=1e13, eeg=1e6) .
These defaults will scale magnetometers and gradiometers
at the same unit.
n_jobs : int (default 1)
Number of jobs to run in parallel.
return_estimators : bool (default False)
Whether to return all estimators or the best. Only considered if
method equals ‘auto’ or is a list of str. Defaults to False
verbose : bool  str  int  or None (default None)
If not None, override default verbose level (see mne.verbose).
