epochs : instance of Epochs
inverse_operator : instance of InverseOperator
frequencies : array
Array of frequencies of interest.
label : Label
Restricts the source estimates to a given label.
lambda2 : float
The regularization parameter of the minimum norm.
method : “MNE”  “dSPM”  “sLORETA”
Use mininum norm, dSPM or sLORETA.
nave : int
The number of averages used to scale the noise covariance matrix.
n_cycles : float  array of float
Number of cycles. Fixed number or one per frequency.
decim : int
Temporal decimation factor.
use_fft : bool
Do convolutions in time or frequency domain with FFT.
pick_ori : None  “normal”
If “normal”, rather than pooling the orientations by taking the norm,
only the radial component is kept. This is only implemented
when working with loose orientations.
baseline : None (default) or tuple of length 2
The time interval to apply baseline correction.
If None do not apply it. If baseline is (a, b)
the interval is between “a (s)” and “b (s)”.
If a is None the beginning of the data is used
and if b is None then b is set to the end of the interval.
If baseline is equal ot (None, None) all the time
interval is used.
baseline_mode : None  ‘logratio’  ‘zscore’
Do baseline correction with ratio (power is divided by mean
power during baseline) or zscore (power is divided by standard
deviation of power during baseline after subtracting the mean,
power = [power  mean(power_baseline)] / std(power_baseline)).
pca : bool
If True, the true dimension of data is estimated before running
the timefrequency transforms. It reduces the computation times
e.g. with a dataset that was maxfiltered (true dim is 64).
n_jobs : int
Number of jobs to run in parallel.
zero_mean : bool
Make sure the wavelets are zero mean.
prepared : bool
If True, do not call prepare_inverse_operator.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
