mne.beamformer.tf_dics¶

mne.beamformer.
tf_dics
(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths, subtract_evoked=False, mode='fourier', freq_bins=None, frequencies=None, n_ffts=None, mt_bandwidths=None, mt_adaptive=False, mt_low_bias=True, cwt_n_cycles=7, decim=1, reg=0.05, label=None, pick_ori=None, rank=None, inversion='single', weight_norm=None, depth=1.0, real_filter=False, reduce_rank=False, verbose=None)[source]¶ 5D timefrequency beamforming based on DICS.
Calculate source power in timefrequency windows using a spatial filter based on the Dynamic Imaging of Coherent Sources (DICS) beamforming approach 1. For each time window and frequency bin combination, crossspectral density (CSD) is computed and used to create a DICS beamformer spatial filter.
 Parameters
 epochs
Epochs
Single trial epochs.
 forward
dict
Forward operator.
 noise_csds
list
of instances ofCrossSpectralDensity
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
offloat
Time window lengths in seconds. One time window length should be provided for each frequency bin.
 subtract_evokedbool
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
oftuple
offloat
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
offloat
list
oflist
offloat
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 betweentmin
andtmax
will be used. Only used in ‘multitaper’ or ‘fourier’ mode. mt_bandwidths
list
offloat
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_adaptivebool
Use adaptive weights to combine the tapered spectra into CSD. Only used in ‘multitaper’ mode. Defaults to False.
 mt_low_biasbool
Only use tapers with more than 90% spectral concentration within bandwidth. Only used in ‘multitaper’ mode. Defaults to True.
 cwt_n_cycles
float
list
offloat
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.
 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, unlessreg=0
is used. The default is None.New in version 0.17.
 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. Wheninversion='matrix'
, all three dipoles at a source vertex are considered as a group and the spatial filters are computed jointly using a matrix inversion. Whileinversion='single'
is more stable,inversion='matrix'
is more precise. See Notes ofmake_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
. depth
None
float
dict
How to weight (or normalize) the forward using a depth prior. If float (default 0.8), it acts as the depth weighting exponent (
exp
) to use, which must be between 0 and 1. None is equivalent to 0, meaning no depth weighting is performed. It can also be adict
containing keyword arguments to pass tomne.forward.compute_depth_prior()
(see docstring for details and defaults). This is effectively ignored whenmethod='eLORETA'
.Changed in version 0.20: Depth bias ignored for
method='eLORETA'
. real_filterbool
If
True
, take only the real part of the crossspectraldensity matrices to compute real filters. Defaults toFalse
. reduce_rankbool
If True, the rank of the denominator of the beamformer formula (i.e., during pseudoinversion) will be reduced by one for each spatial location. Setting
reduce_rank=True
is typically necessary if you use a single sphere model with MEG data.Changed in version 0.20: Support for reducing rank in all modes (previously only supported
pick='max_power'
with weight normalization). verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only.
 epochs
 Returns
 stcs
list
ofSourceEstimate
VolSourceEstimate
Source power at each time window. One SourceEstimate object is returned for each frequency bin.
 stcs
Notes
Dalal et al. 1 used a synthetic aperture magnetometry beamformer (SAM) in each timefrequency window instead of DICS.
An alternative to using noise CSDs is to normalize the forward solution (
depth
) or the beamformer weights (weight_norm
). In this case,noise_csds
may be set toNone
.References
 1(1,2)
Sarang S. Dalal, Adrian G. Guggisberg, Erik Edwards, Kensuke Sekihara, Anne M. Findlay, Ryan T. Canolty, Mitchel S. Berger, Robert T. Knight, Nicholas M. Barbaro, Heidi E. Kirsch, and Srikantan S. Nagarajan. Fivedimensional neuroimaging: localization of the time–frequency dynamics of cortical activity. NeuroImage, 40(4):1686–1700, 2008. doi:10.1016/j.neuroimage.2008.01.023.
 2
Kensuke Sekihara and Srikantan S. Nagarajan. Adaptive Spatial Filters for Electromagnetic Brain Imaging. Series in Biomedical Engineering. Springer, Berlin; Heidelberg, 2008. ISBN 9783540793694 9783540793700. doi:10.1007/9783540793700.