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, 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:

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’ 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 invidual labels. n_svd_comp : integer 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. verbose : bool, str, int, or None If not None, override default verbose level (see `mne.verbose()` and Logging documentation for more).
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).

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’

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 invidual labels.

n_svd_comp : integer

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.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

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).

Examples using `mne.minimum_norm.point_spread_function`¶

Compute point-spread functions (PSFs) for MNE/dSPM/sLORETA

mne.minimum_norm.point_spread_function¶

Examples using mne.minimum_norm.point_spread_function¶

Examples using `mne.minimum_norm.point_spread_function`¶