mne.beamformer.make_dics

mne.beamformer.make_dics(info, forward, csd, reg=0.05, noise_csd=None, label=None, pick_ori=None, rank=None, weight_norm=None, reduce_rank=False, depth=1.0, real_filter=True, inversion='matrix', 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 (see apply_dics_csd()), the source power can be estimated for each source point.

Parameters
infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

forwardinstance of Forward

Forward operator.

csdinstance of CrossSpectralDensity

The data cross-spectral density (CSD) matrices. A source estimate is performed for each frequency or frequency-bin defined in the CSD object.

regfloat

The regularization to apply to the cross-spectral density before computing the inverse.

noise_csdinstance of 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.

labelLabel | None

Restricts the solution to a given label.

pick_oriNone | 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.

rankNone | ‘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.

weight_normstr | 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-invariante'

    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:

    1. Is rotation invariant ('unit-noise-gain' is not);

    2. Satisfies the first requirement from 2 that w @ w.conj().T == I, whereas 'unit-noise-gain' has non-zero off-diagonals; but

    3. 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.

reduce_rankbool

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).

depthNone | 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'.

real_filterbool

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 deprecated False as default for real_filter. With version 0.24, True is the new default.

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 section 5 of 4. Defaults to 'matrix'.

Changed in version 0.21: Default changed to 'matrix'.

verbosebool | str | int | None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns
filtersinstance of 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

apply_dics_csd

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

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(1,2,3,4)

Kensuke Sekihara and Srikantan S. Nagarajan. Adaptive Spatial Filters for Electromagnetic Brain Imaging. Series in Biomedical Engineering. Springer, Berlin; Heidelberg, 2008. ISBN 978-3-540-79369-4 978-3-540-79370-0. doi:10.1007/978-3-540-79370-0.

3

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.

4(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 group-level statistics in Python. bioRxiv, 2018. doi:10.1101/245530.

5

Joerg F. Hipp, Andreas K. Engel, and Markus Siegel. Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron, 69(2):387–396, 2011. doi:10.1016/j.neuron.2010.12.027.