mne.beamformer.
tf_lcmv
(epochs, forward, noise_covs, tmin, tmax, tstep, win_lengths, freq_bins, subtract_evoked=False, reg=0.05, label=None, pick_ori=None, n_jobs=1, picks=None, rank=None, weight_norm='unit-noise-gain', raw=None, verbose=None)[source]¶5D time-frequency beamforming based on LCMV.
Calculate source power in time-frequency windows using a spatial filter based on the Linearly Constrained Minimum Variance (LCMV) beamforming approach [R7071]. Band-pass filtered epochs are divided into time windows from which covariance is computed and used to create a beamformer spatial filter.
Note
This implementation has not been heavily tested so please report any issues or suggestions.
Parameters: | epochs : Epochs
forward : dict
noise_covs : list of instances of Covariance | None
tmin : float
tmax : float
tstep : float
win_lengths : list of float
freq_bins : list of tuples of float
subtract_evoked : bool
reg : float
label : Label | None
pick_ori : None | ‘normal’
n_jobs : int | str
picks : array-like of int
rank : None | int | dict
weight_norm : ‘unit-noise-gain’ | None raw : instance of Raw | None
verbose : bool, str, int, or None
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Returns: | stcs : list of SourceEstimate
|
References
[R7071] | (1, 2) Dalal et al. Five-dimensional neuroimaging: Localization of the time-frequency dynamics of cortical activity. NeuroImage (2008) vol. 40 (4) pp. 1686-1700 |
[R7171] | (1, 2, 3) Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media |
mne.beamformer.tf_lcmv
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