# mne.beamformer.lcmv_epochs¶

mne.beamformer.lcmv_epochs(epochs, forward, noise_cov, data_cov, reg=0.01, label=None, pick_ori=None, return_generator=False, picks=None, rank=None, verbose=None)

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: 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). stc: list | generator of (SourceEstimate | VolSourceEstimate) : The source estimates for all epochs