mne.beamformer.lcmv_epochs¶

mne.beamformer.lcmv_epochs(epochs, forward, noise_cov, data_cov, reg=0.05, label=None, pick_ori=None, return_generator=False, picks=None, rank=None, verbose=None)[source]¶

Linearly Constrained Minimum Variance (LCMV) beamformer.

Compute Linearly Constrained Minimum Variance (LCMV) beamformer on single trial data.

Note

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

Parameters:

Parameters:	epochs : Epochs Single trial epochs. forward : dict Forward operator. noise_cov : Covariance The noise covariance. data_cov : Covariance The data covariance. reg : float The regularization for the whitened data covariance. label : Label Restricts the LCMV solution to a given label. pick_ori : None \| ‘normal’ \| ‘max-power’ If ‘normal’, rather than pooling the orientations by taking the norm, only the radial component is kept. If ‘max-power’, the source orientation that maximizes output source power is chosen. 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. picks : array-like of int Channel indices to use for beamforming (if None all channels are used except bad channels). rank : None \| int \| dict Specified rank of the noise covariance matrix. If None, the rank is detected automatically. If int, the rank is specified for the MEG channels. A dictionary with entries ‘eeg’ and/or ‘meg’ can be used to specify the rank for each modality. verbose : bool, str, int, or None If not None, override default verbose level (see `mne.verbose()` and Logging documentation for more).
Returns:	stc: list \| generator of (SourceEstimate \| VolSourceEstimate) The source estimates for all epochs

epochs : Epochs

Single trial epochs.

forward : dict

Forward operator.

noise_cov : Covariance

The noise covariance.

data_cov : Covariance

The data covariance.

reg : float

The regularization for the whitened data covariance.

label : Label

Restricts the LCMV solution to a given label.

pick_ori : None | ‘normal’ | ‘max-power’

If ‘normal’, rather than pooling the orientations by taking the norm, only the radial component is kept. If ‘max-power’, the source orientation that maximizes output source power is chosen.

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.

picks : array-like of int

Channel indices to use for beamforming (if None all channels are used except bad channels).

rank : None | int | dict

Specified rank of the noise covariance matrix. If None, the rank is detected automatically. If int, the rank is specified for the MEG channels. A dictionary with entries ‘eeg’ and/or ‘meg’ can be used to specify the rank for each modality.

verbose : bool, str, int, or None

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

Returns:

stc: list | generator of (SourceEstimate | VolSourceEstimate)

The source estimates for all epochs