mne.beamformer.dics(evoked, forward, noise_csd, data_csd, reg=0.05, label=None, pick_ori=None, real_filter=False, verbose=None)[source]

Dynamic Imaging of Coherent Sources (DICS).

Compute a Dynamic Imaging of Coherent Sources (DICS) [R19] beamformer on evoked data and return estimates of source time courses.


Fixed orientation forward operators with real_filter=False will result in complex time courses, in which case absolute values will be returned.


This implementation has not been heavily tested so please report any issues or suggestions.


evoked : Evoked

Evoked data.

forward : dict

Forward operator.

noise_csd : instance of CrossSpectralDensity

The noise cross-spectral density.

data_csd : instance of CrossSpectralDensity

The data cross-spectral density.

reg : float

The regularization for the cross-spectral density.

label : Label | None

Restricts the solution to a given label.

pick_ori : None | ‘normal’

If ‘normal’, rather than pooling the orientations by taking the norm, only the radial component is kept.

real_filter : bool

If True, take only the real part of the cross-spectral-density matrices to compute real filters as in [R20]. Default is False.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).


stc : SourceEstimate | VolSourceEstimate

Source time courses

See also



For more information about real_filter, see the supplemental information from [R20].


[R19](1, 2) Gross et al. Dynamic imaging of coherent sources: Studying neural interactions in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699
[R20](1, 2, 3) Hipp JF, Engel AK, Siegel M (2011) Oscillatory Synchronization in Large-Scale Cortical Networks Predicts Perception. Neuron 69:387-396.

Examples using mne.beamformer.dics