mne.beamformer.make_dics¶

mne.beamformer.
make_dics
(info, forward, csd, reg=0.05, label=None, pick_ori=None, rank=None, inversion='single', weight_norm=None, normalize_fwd=True, real_filter=False, reduce_rank=False, verbose=None)[source]¶ Compute a Dynamic Imaging of Coherent Sources (DICS) spatial filter.
This is a beamformer filter that can be used to estimate the source power at a specific frequency range 1. It does this by constructing a spatial filter for each source point. The computation of these filters is very similar to those of the LCMV beamformer (
make_lcmv()
), but instead of operating on a covariance matrix, the CSD matrix is used. When applying these filters to a CSD matrix (seeapply_dics_csd()
), the source power can be estimated for each source point. Parameters
 infoinstance of
Info
Measurement info, e.g.
epochs.info
. forwardinstance of
Forward
Forward operator.
 csdinstance of
CrossSpectralDensity
The data crossspectral density (CSD) matrices. A source estimate is performed for each frequency or frequencybin defined in the CSD object.
 reg
float
The regularization to apply to the crossspectral density before computing the inverse.
 label
Label
None
Restricts the solution to a given label.
 pick_ori
None
 ‘normal’  ‘maxpower’ The source orientation to compute the filter 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.
 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 section 5 of 2. Defaults to ‘single’. weight_norm‘unitnoisegain’  ‘nai’ 
None
If ‘unitnoisegain’, the unitnoise gain minimum variance beamformer will be computed (BorgiottiKaplan beamformer) 3. If ‘nai’, the Neural Activity Index 4 will be computed. Defaults to
None
, in which case no normalization is performed. normalize_fwdbool
Whether to normalize the forward solution. Defaults to
True
. Note that this normalization is not required when weight normalization (weight_norm
) is used. 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).
 infoinstance of
 Returns
 filtersinstance of
Beamformer
Dictionary containing filter weights from DICS beamformer. Contains the following keys:
 ‘weights’ndarray, shape (n_frequencies, n_weights)
For each frequency, the filter weights of the beamformer.
 ‘csd’instance of CrossSpectralDensity
The data crossspectral density matrices used to compute the beamformer.
 ‘ch_names’list of str
Channels used to compute the beamformer.
 ‘proj’ndarray, shape (n_channels, n_channels)
Projections used to compute the beamformer.
 ‘vertices’list of ndarray
Vertices for which the filter weights were computed.
 ‘inversion’‘single’  ‘matrix’
Whether the spatial filters were computed for each dipole separately or jointly for all dipoles at each vertex using a matrix inversion.
 ‘weight_norm’None  ‘unitnoisegain’
The normalization of the weights.
 ‘normalize_fwd’bool
Whether the forward solution was normalized
 ‘n_orient’int
Number of source orientations defined in the forward model.
 ‘subject’str
The subject ID.
 ‘src_type’str
Type of source space.
 filtersinstance of
See also
Notes
The original reference is 1. See 2 for a tutorial style paper on the topic.
The DICS beamformer is very similar to the LCMV (
make_lcmv()
) beamformer and many of the parameters are shared. However,make_dics()
andmake_lcmv()
currently have different defaults for these parameters, which were settled on separately through extensive practical use case testing (but not necessarily exhaustive parameter space searching), and it remains to be seen how functionally interchangeable they could be.The default setting reproduce the DICS beamformer as described in 2:
inversion='single', weight_norm=None, normalize_fwd=True
To use the
make_lcmv()
defaults, use:inversion='matrix', weight_norm='unitgain', normalize_fwd=False
For more information about
real_filter
, see the supplemental information from 5.References
 1(1,2)
Joachim Groß, Jan Kujala, Matti S. Hämäläinen, Lars Timmermann, Alfons Schnitzler, and Riitta Salmelin. Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proceedings of the National Academy of Sciences, 98(2):694–699, 2001. doi:10.1073/pnas.98.2.694.
 2(1,2,3)
Marijn van Vliet, Mia Liljeström, Susanna Aro, Riitta Salmelin, and Jan Kujala. Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to grouplevel statistics in Python. bioRxiv, 2018. doi:10.1101/245530.
 3
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.
 4
Barry D. Van Veen, Wim van Drongelen, Moshe Yuchtman, and Akifumi Suzuki. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on Biomedical Engineering, 44(9):867–880, 1997. doi:10.1109/10.623056.
 5
Joerg F. Hipp, Andreas K. Engel, and Markus Siegel. Oscillatory synchronization in largescale cortical networks predicts perception. Neuron, 69(2):387–396, 2011. doi:10.1016/j.neuron.2010.12.027.