mne.minimum_norm.point_spread_function¶

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]¶ Compute pointspread functions (PSFs) for linear estimators.
Compute pointspread functions (PSF) in labels for a combination of inverse operator and forward solution. PSFs are computed for test sources that are perpendicular to cortical surface.
 Parameters
 inverse_operatorinstance 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
ofLabel
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 subleadfields for labels This corresponds to situations where labels can be assumed to be homogeneously activated. ‘svd’: SVD components of subleadfields for labels This is better suited for situations where activation patterns are assumed to be more variable. “subleadfields” 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 subleadfields are shown in screen output.
 use_cps
None
 bool (defaultTrue
) Whether to use cortical patch statistics to define normal orientations. Only used when surf_ori and/or force_fixed are True.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 inverse_operatorinstance of
 Returns
 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 noisenormalized 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).
 stc_psf