mne.beamformer.dics

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.

Note

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

Note

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

Parameters:

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).
Returns:

stc : SourceEstimate | VolSourceEstimate

Source time courses

See also

dics_epochs

Notes

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

References

[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