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 point-spread functions (PSFs) for linear estimators.
Compute point-spread 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 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 (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 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).
- stc_psf