tf_mixed_norm(evoked, forward, noise_cov, loose='auto', depth=0.8, maxit=3000, tol=0.0001, weights=None, weights_min=0.0, pca=True, debias=True, wsize=64, tstep=4, window=0.02, return_residual=False, return_as_dipoles=False, alpha=None, l1_ratio=None, dgap_freq=10, rank=None, pick_ori=None, n_tfmxne_iter=1, verbose=None)¶
Time-Frequency Mixed-norm estimate (TF-MxNE).
- evokedinstance of
Evoked data to invert.
- noise_covinstance of
Noise covariance to compute whitener.
float| ‘auto’ |
Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. Can be:
- float between 0 and 1 (inclusive)
If 0, then the solution is computed with fixed orientation. If 1, it corresponds to free orientations.
Uses 0.2 for surface source spaces (unless
fixedis True) and 1.0 for other source spaces (volume or mixed).
Mapping from the key for a given source space type (surface, volume, discrete) to the loose value. Useful mostly for mixed source spaces.
How to weight (or normalize) the forward using a depth prior. If float (default 0.8), it acts as the depth weighting exponent (
exp) to use, which must be between 0 and 1. None is equivalent to 0, meaning no depth weighting is performed. It can also be a
dictcontaining keyword arguments to pass to
mne.forward.compute_depth_prior()(see docstring for details and defaults). This is effectively ignored when
Changed in version 0.20: Depth bias ignored for
Maximum number of iterations.
Weight for penalty in mixed_norm. Can be None or 1d array of length n_sources or a SourceEstimate e.g. obtained with wMNE or dSPM or fMRI.
Do not consider in the estimation sources for which weights is less than weights_min.
If True the rank of the data is reduced to true dimension.
Remove coefficient amplitude bias due to L1 penalty.
Length of the STFT window in samples (must be a multiple of 4). If an array is passed, multiple TF dictionaries are used (each having its own wsize and tstep) and each entry of wsize must be a multiple of 4. See .
Step between successive windows in samples (must be a multiple of 2, a divider of wsize and smaller than wsize/2) (default: wsize/2). If an array is passed, multiple TF dictionaries are used (each having its own wsize and tstep), and each entry of tstep must be a multiple of 2 and divide the corresponding entry of wsize. See .
Length of time window used to take care of edge artifacts in seconds. It can be one float or float if the values are different for left and right window length.
If True, the residual is returned as an Evoked instance.
If True, the sources are returned as a list of Dipole instances.
floatin [0, 100) or
Overall regularization parameter. If alpha and l1_ratio are not None, alpha_space and alpha_time are overridden by alpha * alpha_max * (1. - l1_ratio) and alpha * alpha_max * l1_ratio. 0 means no regularization, 100 would give 0 active dipole.
floatin [0, 1] or
Proportion of temporal regularization. If l1_ratio and alpha are not None, alpha_space and alpha_time are overridden by alpha * alpha_max * (1. - l1_ratio) and alpha * alpha_max * l1_ratio. 0 means no time regularization a.k.a. MxNE.
The duality gap is evaluated every dgap_freq iterations.
dict| ‘info’ | ‘full’
This controls the rank computation that can be read from the measurement info or estimated from the data. See
mne.compute_rank()for details.The default is None.
New in version 0.18.
None| “normal” | “vector”
Pooling is performed by taking the norm of loose/free orientations. In case of a fixed source space no norm is computed leading to signed source activity.
Only the normal to the cortical surface is kept. This is only implemented when working with loose orientations.
No pooling of the orientations is done, and the vector result will be returned in the form of a
Number of TF-MxNE iterations. If > 1, iterative reweighting is applied.
- evokedinstance of
A. Gramfort, D. Strohmeier, J. Haueisen, M. Hämäläinen, M. Kowalski “Time-Frequency Mixed-Norm Estimates: Sparse M/EEG imaging with non-stationary source activations”, Neuroimage, Volume 70, pp. 410-422, 15 April 2013. DOI: 10.1016/j.neuroimage.2012.12.051
A. Gramfort, D. Strohmeier, J. Haueisen, M. Hämäläinen, M. Kowalski “Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries”, Proceedings Information Processing in Medical Imaging Lecture Notes in Computer Science, Volume 6801/2011, pp. 600-611, 2011. DOI: 10.1007/978-3-642-22092-0_49
Y. Bekhti, D. Strohmeier, M. Jas, R. Badeau, A. Gramfort. “M/EEG source localization with multiscale time-frequency dictionaries”, 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016. DOI: 10.1109/PRNI.2016.7552337