Parameters: 
 epochs : Epochs
Single trial epochs.
 forward : dict
Forward operator.
 noise_csds : list of instances of CrossSpectralDensity  None
Noise crossspectral density for each frequency bin. If these are
specified, the DICS filters will be applied to both the signal and
noise CSDs. The source power estimates for each frequency bin will be
scaled by the estimated noise power (signal / noise).
Specifying None will disable performing noise normalization.
 tmin : float
Minimum time instant to consider.
 tmax : float
Maximum time instant to consider.
 tstep : float
Spacing between consecutive time windows, should be smaller than or
equal to the shortest time window length.
 win_lengths : list of float
Time window lengths in seconds. One time window length should be
provided for each frequency bin.
 subtract_evoked : bool
If True, subtract the averaged evoked response prior to computing the
tf source grid. Defaults to False.
 mode : ‘fourier’  ‘multitaper’  ‘cwt_morlet’
Spectrum estimation mode. Defaults to ‘fourier’.
 freq_bins : list of tuple of float
Start and end point of frequency bins of interest.
Only used in ‘multitaper’ or ‘fourier’ mode. For ‘cwt_morlet’ mode, use
the frequencies parameter instead.
 frequencies : list of float  list of list of float
The frequencies to compute the source power for. If you want to compute
the average power for multiple frequency bins, specify a list of
lists: each list containing the frequencies for the corresponding bin.
Only used in ‘cwt_morlet’ mode. In other modes, use the freq_bins
parameter instead.
 n_ffts : list  None
Length of the FFT for each frequency bin. If None (the default),
the exact number of samples between tmin and tmax will be used.
Only used in ‘multitaper’ or ‘fourier’ mode.
 mt_bandwidths : list of float
The bandwidths of the multitaper windowing function in Hz. Only used in
‘multitaper’ mode. One value should be provided for each frequency bin.
Defaults to None.
 mt_adaptive : bool
Use adaptive weights to combine the tapered spectra into CSD. Only used
in ‘multitaper’ mode. Defaults to False.
 mt_low_bias : bool
Only use tapers with more than 90{‘verbose’: ‘n verbose : bool, str, int, or Nonen If not None, override default verbose level (see mne.verbose() n and Logging documentation for more).’, ‘verbose_meth’: ‘n verbose : bool, str, int, or Nonen If not None, override default verbose level (see mne.verbose() n and Logging documentation for more). Defaults to self.verbose.’, ‘picks_header’: ‘picks : str  list  slice  None’, ‘picks_base’: ‘picks : str  list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\'] ) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\'] n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick ‘, ‘picks_all’: ‘picks : str  list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\'] ) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\'] n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick all channels.’, ‘picks_all_data’: ‘picks : str  list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\'] ) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\'] n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick all data channels.’, ‘picks_all_data_noref’: ‘picks : str  list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\'] ) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\'] n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick all data channels(excluding reference MEG channels).’, ‘picks_good_data’: ‘picks : str  list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\'] ) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\'] n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick good data channels.’, ‘picks_good_data_noref’: ‘picks : str  list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. In lists, channel type stringsn (e.g., [\'meg\', \'eeg\'] ) will pick channels of thosen types, channel name strings (e.g., [\'MEG0111\', \'MEG2623\'] n will pick the given channels. Can also be the string valuesn “all” to pick all channels, or “data” to pick data channels.n None (default) will pick good data channels(excluding reference MEG channels).’, ‘picks_nostr’: ‘n picks : list  slice  Nonen Channels to include. Slices and lists of integers will ben interpreted as channel indices. None (default) will pick all channels.’}pectral concentration within
bandwidth. Only used in ‘multitaper’ mode. Defaults to True.
 cwt_n_cycles: float  list of float  None
Number of cycles to use when constructing Morlet wavelets. Fixed number
or one per frequency. Defaults to 7.
Only used in ‘cwt_morlet’ mode.
 decim : int  slice
To reduce memory usage, decimation factor during timefrequency
decomposition. Defaults to 1 (no decimation).
Only used in ‘cwt_morlet’ mode.
If int, uses tfr[…, ::decim].
If slice, uses tfr[…, decim].
 reg : float
Regularization to use for the DICS beamformer computation.
Defaults to 0.05.
 label : Label  None
Restricts the solution to a given label. Defaults to None.
 pick_ori : None  ‘normal’  ‘maxpower’
The source orientation to estimate source power for:
None :
orientations are pooled. (Default)
 ‘normal’ :
filters are computed for the orientation tangential to the
cortical surface
 ‘maxpower’ :
filters are computer for the orientation that maximizes
spectral power.
Defaults to None .
 rank : None  int  ‘full’
This controls the effective rank of the covariance matrix when
computing the inverse. The rank can be set explicitly by specifying an
integer value. If None , the rank will be automatically estimated.
Since applying regularization will always make the covariance matrix
full rank, the rank is estimated before regularization in this case. If
‘full’, the rank will be estimated after regularization and hence
will mean using the full rank, unless reg=0 is used.
The default is None.
 inversion : ‘single’  ‘matrix’
This determines how the beamformer deals with source spaces in “free”
orientation. Such source spaces define three orthogonal dipoles at each
source point. When inversion='single' , each dipole is considered
as an individual source and the corresponding spatial filter is
computed for each dipole separately. When inversion='matrix' , all
three dipoles at a source vertex are considered as a group and the
spatial filters are computed jointly using a matrix inversion. While
inversion='single' is more stable, inversion='matrix' is more
precise. See Notes of make_dics() . Defaults to ‘single’.
 weight_norm : None  ‘unitnoisegain’
How to normalize the beamformer weights. None means no normalization is
performed. If ‘unitnoisegain’, the unitnoise gain minimum variance
beamformer will be computed (BorgiottiKaplan beamformer) [2].
Defaults to None .
 normalize_fwd : bool
Whether to normalize the forward solution. Defaults to True . Note
that this normalization is not required when weight normalization
(weight_norm ) is used.
 real_filter : bool
If True , take only the real part of the crossspectraldensity
matrices to compute real filters. Defaults to False .
 reduce_rank : bool
If True , the rank of the forward operator will be reduced by 1 for
each spatial location, prior to inversion. This may be necessary when
you use a single sphere model for MEG and mode='vertex' .
Defaults to False .
 verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
