mne.beamformer.dics_epochs(epochs, forward, noise_csd, data_csd, reg=0.01, label=None, pick_ori=None, return_generator=False, verbose=None)

Dynamic Imaging of Coherent Sources (DICS).

Compute a Dynamic Imaging of Coherent Sources (DICS) beamformer on single trial data and return estimates of source time courses.

NOTE : Fixed orientation forward operators will result in complex time courses in which case absolute values will be returned. Therefore the orientation will no longer be fixed.

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


epochs : Epochs

Single trial epochs.

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.

return_generator : bool

Return a generator object instead of a list. This allows iterating over the stcs without having to keep them all in memory.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).


stc: list | generator of SourceEstimate | VolSourceEstimate :

The source estimates for all epochs

See also



The original reference is: Gross et al. Dynamic imaging of coherent sources: Studying neural interactions in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699