This documentation is for development version 0.18.dev0.

mne.minimum_norm.point_spread_function(inverse_operator, forward, labels, method='dSPM', lambda2=0.1111111111111111, pick_ori=None, mode='mean', n_svd_comp=1, use_cps=True, verbose=None)[source]
Parameters: inverse_operator : instance of InverseOperator Inverse operator. forward : dict Forward solution. Note: (Bad) channels not included in forward solution will not be used in PSF computation. labels : list of Label Labels for which PSFs shall be computed. method : ‘MNE’ | ‘dSPM’ | ‘sLORETA’ | ‘eLORETA’ Inverse method for which PSFs shall be computed (for apply_inverse()). lambda2 : float The regularization parameter (for apply_inverse()). pick_ori : None | “normal” If “normal”, rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations (for apply_inverse()). mode : ‘mean’ | ‘sum’ | ‘svd’ PSFs can be computed for different summary measures with labels: ‘sum’ or ‘mean’: sum or means of sub-leadfields for labels This corresponds to situations where labels can be assumed to be homogeneously activated. ‘svd’: SVD components of sub-leadfields for labels This is better suited for situations where activation patterns are assumed to be more variable. “sub-leadfields” are the parts of the forward solutions that belong to vertices within individual labels. n_svd_comp : int Number of SVD components for which PSFs will be computed and output (irrelevant for ‘sum’ and ‘mean’). Explained variances within sub-leadfields are shown in screen output. use_cps : None | bool (default True) Whether to use cortical patch statistics to define normal orientations. Only used when surf_ori and/or force_fixed are True. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more). stc_psf : SourceEstimate The PSFs for the specified labels If mode=’svd’: n_svd_comp components per label are created (i.e. n_svd_comp successive time points in mne_analyze) The last sample is the summed PSF across all labels Scaling of PSFs is arbitrary, and may differ greatly among methods (especially for MNE compared to noise-normalized estimates). evoked_fwd : Evoked Forward solutions corresponding to PSFs in stc_psf If mode=’svd’: n_svd_comp components per label are created (i.e. n_svd_comp successive time points in mne_analyze) The last sample is the summed forward solution across all labels (sum is taken across summary measures).