epochs : instance of Epochs
inverse_operator : instance of inverse operator
bands : dict
Example : bands = dict(alpha=[8, 9]).
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.
df : float
delta frequency within bands.
use_fft : bool
Do convolutions in time or frequency domain with FFT.
decim : int
Temporal decimation factor.
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 to (None, None)
all the time interval is used.
baseline_mode : ‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’ | None
Perform baseline correction by
- subtracting the mean baseline power (‘mean’)
- dividing by the mean baseline power (‘ratio’)
- dividing by the mean baseline power and taking the log (‘logratio’)
- subtracting the mean baseline power followed by dividing by the
mean baseline power (‘percent’)
- subtracting the mean baseline power and dividing by the standard
deviation of the baseline power (‘zscore’)
- dividing by the mean baseline power, taking the log, and dividing
by the standard deviation of the baseline power (‘zlogratio’)
If None no baseline correction is applied.
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.
prepared : bool
If True, do not call prepare_inverse_operator.
verbose : bool, str, int, or None
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