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 time-frequency 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
|
