LORETA#

class invert.solvers.loreta.SolverELORETA(name='Exact Low Resolution Tomography', **kwargs)#
Class for the exact Low Resolution Tomography (eLORETA) inverse

solution [1].

References

[1] Pascual-Marqui, R. D. (2007). Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. arXiv preprint arXiv:0710.3341.

make_inverse_operator(forward, *args, alpha='auto', verbose=0, stop_crit=0.005, max_iter=100, **kwargs)#

Calculate inverse operator.

Parameters

  • forward (mne.Forward) – The mne-python Forward model instance.

  • alpha (float) – The regularization parameter.

  • stop_crit (float) – The convergence criterion to optimize the weight matrix.

  • max_iter (int) – The stopping criterion to optimize the weight matrix.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.loreta.SolverLORETA(name='Low Resolution Tomography', **kwargs)#

Class for the Low Resolution Tomography (LORETA) inverse solution.

References

[1] Pascual-Marqui, R. D. (1999). Review of methods for solving the EEG inverse problem. International journal of bioelectromagnetism, 1(1), 75-86.

make_inverse_operator(forward, *args, alpha='auto', **kwargs)#

Calculate inverse operator.

Parameters

  • forward (mne.Forward) – The mne-python Forward model instance.

  • alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.loreta.SolverSLORETA(name='Standardized Low Resolution Tomography', **kwargs)#
Class for the standardized Low Resolution Tomography (sLORETA) inverse

solution [1].

References

[1] Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol, 24(Suppl D), 5-12.

make_inverse_operator(forward, *args, alpha=0.01, verbose=0, **kwargs)#

Calculate inverse operator.

Parameters

  • forward (mne.Forward) – The mne-python Forward model instance.

  • alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

invert.solvers.loreta.calc_eloreta_D2(leadfield, noise_cov, alpha, stop_crit=0.005, verbose=0)#

Algorithm that optimizes weight matrix D as described in Assessing interactions in the brain with exactlow-resolution electromagnetic tomography; Pascual-Marqui et al. 2011 and https://www.sciencedirect.com/science/article/pii/S1053811920309150

class invert.solvers.smap.SolverSMAP(name='S-MAP', **kwargs)#
Class for the Quadratic regularization and spatial regularization

(S-MAP) inverse solution [1].

References

[1] Baillet, S., & Garnero, L. (1997). A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE transactions on Biomedical Engineering, 44(5), 374-385.

make_inverse_operator(forward, *args, alpha='auto', **kwargs)#

Calculate inverse operator.

Parameters

  • forward (mne.Forward) – The mne-python Forward model instance.

  • alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

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