mne.minimum_norm.
apply_inverse
(evoked, inverse_operator, lambda2=0.1111111111111111, method='dSPM', pick_ori=None, prepared=False, label=None, method_params=None, return_residual=False, verbose=None)[source]¶Apply inverse operator to evoked data.
Parameters: |
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Returns: |
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See also
apply_inverse_raw
apply_inverse_epochs
Notes
Currently only the method='eLORETA'
has additional options.
It performs an iterative fit with a convergence criterion, so you can
pass a method_params
dict
with string keys mapping to values
for:
- ‘eps’ : float
- The convergence epsilon (default 1e-6).
- ‘max_iter’ : int
- The maximum number of iterations (default 20). If less regularization is applied, more iterations may be necessary.
- ‘force_equal’ : bool
- Force all eLORETA weights for each direction for a given location equal. The default is None, which means
True
for loose-orientation inverses andFalse
for free- and fixed-orientation inverses. See below.
The eLORETA paper [4] defines how to compute inverses for fixed- and
free-orientation inverses. In the free orientation case, the X/Y/Z
orientation triplet for each location is effectively multiplied by a
3x3 weight matrix. This is the behavior obtained with
force_equal=False
parameter.
However, other noise normalization methods (dSPM, sLORETA) multiply all
orientations for a given location by a single value.
Using force_equal=True
mimics this behavior by modifying the iterative
algorithm to choose uniform weights (equivalent to a 3x3 diagonal matrix
with equal entries).
It is necessary to use force_equal=True
with loose orientation inverses (e.g., loose=0.2
), otherwise the
solution resembles a free-orientation inverse (loose=1.0
).
It is thus recommended to use force_equal=True
for loose orientation
and force_equal=False
for free orientation inverses. This is the
behavior used when the parameter force_equal=None
(default behavior).
References
[1] | (1, 2) Hamalainen M S and Ilmoniemi R. Interpreting magnetic fields of the brain: minimum norm estimates. Medical & Biological Engineering & Computing, 32(1):35-42, 1994. |
[2] | (1, 2) Dale A, Liu A, Fischl B, Buckner R. (2000) Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26:55-67. |
[3] | (1, 2) Pascual-Marqui RD (2002), Standardized low resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacology, 24(D):5-12. |
[4] | (1, 2, 3) Pascual-Marqui RD (2007). Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. arXiv:0710.3341 |
mne.minimum_norm.apply_inverse
¶