mne.beamformer.dics_source_power

mne.beamformer.dics_source_power(info, forward, noise_csds, data_csds, reg=0.05, label=None, pick_ori=None, real_filter=False, verbose=None)[source]

Dynamic Imaging of Coherent Sources (DICS).

Calculate source power in time and frequency windows specified in the calculation of the data cross-spectral density matrix or matrices. Source power is normalized by noise power.

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

Parameters:

info : dict

Measurement info, e.g. epochs.info.

forward : dict

Forward operator.

noise_csds : instance or list of instances of CrossSpectralDensity

The noise cross-spectral density matrix for a single frequency or a list of matrices for multiple frequencies.

data_csds : instance or list of instances of CrossSpectralDensity

The data cross-spectral density matrix for a single frequency or a list of matrices for multiple frequencies.

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.

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 power with frequency instead of time.

Notes

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

Examples using mne.beamformer.dics_source_power