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
(see apply_dics_csd()
), the source power can be estimated for each
source point.
mne.Info
The mne.Info
object with information about the sensors and methods of measurement.
Forward
Forward operator.
CrossSpectralDensity
The data cross-spectral density (CSD) matrices. A source estimate is performed for each frequency or frequency-bin defined in the CSD object.
float
The regularization to apply to the cross-spectral density before computing the inverse.
CrossSpectralDensity
| None
Noise cross-spectral density (CSD) matrices. If provided, whitening will be done. The noise CSDs need to have been computed for the same frequencies as the data CSDs. Providing noise CSDs is mandatory if you mix sensor types, e.g. gradiometers with magnetometers or EEG with MEG.
New in version 0.20.
Label
| None
Restricts the solution to a given label.
None
| str
For forward solutions with fixed orientation, None (default) must be used and a scalar beamformer is computed. For free-orientation forward solutions, a vector beamformer is computed and:
None
Orientations are pooled after computing a vector beamformer (Default).
'normal'
Filters are computed for the orientation tangential to the cortical surface.
'max-power'
Filters are computed for the orientation that maximizes power.
None
| ‘info’ | ‘full’ | dict
This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).
None
The rank will be estimated from the data after proper scaling of different channel types.
'info'
The rank is inferred from info
. If data have been processed
with Maxwell filtering, the Maxwell filtering header is used.
Otherwise, the channel counts themselves are used.
In both cases, the number of projectors is subtracted from
the (effective) number of channels in the data.
For example, if Maxwell filtering reduces the rank to 68, with
two projectors the returned value will be 66.
'full'
The rank is assumed to be full, i.e. equal to the
number of good channels. If a Covariance
is passed, this can
make sense if it has been (possibly improperly) regularized without
taking into account the true data rank.
dict
Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.
This parameter must be a dictionary whose keys correspond to
channel types in the data (e.g. 'meg'
, 'mag'
, 'grad'
,
'eeg'
), and whose values are integers representing the
respective ranks. For example, {'mag': 90, 'eeg': 45}
will assume
a rank of 90
and 45
for magnetometer data and EEG data,
respectively.
The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.
The default is None
.
New in version 0.17.
str
| None
Can be:
None
The unit-gain LCMV beamformer [2] will be computed.
'unit-noise-gain'
The unit-noise gain minimum variance beamformer will be computed
(Borgiotti-Kaplan beamformer) [2],
which is not rotation invariant when pick_ori='vector'
.
This should be combined with
stc.project('pca')
to follow
the definition in [2].
'nai'
The Neural Activity Index [3] will be computed,
which simply scales all values from 'unit-noise-gain'
by a fixed
value.
'unit-noise-gain-invariant'
Compute a rotation-invariant normalization using the matrix square
root. This differs from 'unit-noise-gain'
only when
pick_ori='vector'
, creating a solution that:
Is rotation invariant ('unit-noise-gain'
is not);
Satisfies the first requirement from
[2] that w @ w.conj().T == I
,
whereas 'unit-noise-gain'
has non-zero off-diagonals; but
Does not satisfy the second requirement that w @ G.T = θI
,
which arguably does not make sense for a rotation-invariant
solution.
Defaults to None
, in which case no normalization is performed.
If True, the rank of the denominator of the beamformer formula (i.e.,
during pseudo-inversion) 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).
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 None is equivalent to 0, meaning no depth weighting is performed.
It can also be a dict
containing keyword arguments to pass to
mne.forward.compute_depth_prior()
(see docstring for details and
defaults). This is effectively ignored when method='eLORETA'
.
Changed in version 0.20: Depth bias ignored for method='eLORETA'
.
If True
, take only the real part of the cross-spectral-density
matrices to compute real filters.
Changed in version 0.23: Version 0.23 an earlier used real_filter=False
as the default,
as of version 0.24 True
is the default.
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 section 5 of [4].
Defaults to 'matrix'
.
Changed in version 0.21: Default changed to 'matrix'
.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
Beamformer
Dictionary containing filter weights from DICS beamformer. Contains the following keys:
- ‘kind’str
The type of beamformer, in this case ‘DICS’.
- ‘weights’ndarray, shape (n_frequencies, n_weights)
For each frequency, the filter weights of the beamformer.
- ‘csd’instance of CrossSpectralDensity
The data cross-spectral 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.
- ‘n_sources’int
Number of source location for which the filter weight were computed.
- ‘subject’str
The subject ID.
- ‘pick-ori’None | ‘max-power’ | ‘normal’ | ‘vector’
The orientation in which the beamformer filters 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 | ‘unit-noise-gain’
The normalization of the weights.
- ‘src_type’str
Type of source space.
- ‘is_free_ori’bool
Whether the filter was computed in a fixed direction (pick_ori=’max-power’, pick_ori=’normal’) or not.
- ‘whitener’None | ndarray, shape (n_channels, n_channels)
Whitening matrix, provided if whitening was applied to the covariance matrix and leadfield during computation of the beamformer weights.
- ‘max-power-ori’ndarray, shape (n_sources, 3) | None
When pick_ori=’max-power’, this fields contains the estimated direction of maximum power at each source location.
See also
Notes
The original reference is [1]. See [4] 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()
and make_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 [4]:
inversion='single', weight_norm=None, depth=1.
To use the make_lcmv()
defaults, use:
inversion='matrix', weight_norm='unit-noise-gain-invariant', depth=None
For more information about real_filter
, see the
supplemental information from [5].
References
mne.beamformer.make_dics
#Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM