Electroencephalography (EEG) source imaging aims to reconstruct brain activity maps from the neuroelectric potential difference measured on the skull. To obtain the brain activity map, we need to solve an ill-posed and ill-conditioned inverse problem that requires regularization techniques to make the solution viable. When dealing with real-time applications, dimensionality reduction techniques can be used to reduce the computational load required to evaluate the numerical solution of the EEG inverse problem. To this end, in this paper we use the random dipole sampling method, in which a Monte Carlo technique is used to reduce the number of neural sources. This is equivalent to reducing the number of the unknowns in the inverse problem and can be seen as a first regularization step. Then, we solve the reduced EEG inverse problem with two popular inversion methods, the weighted Minimum Norm Estimate (wMNE) and the standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA). The main result of this paper is the error estimates of the reconstructed activity map obtained with the randomized version of wMNE and sLORETA. Numerical experiments on synthetic EEG data demonstrate the effectiveness of the random dipole sampling method.

Solution of the EEG inverse problem by random dipole sampling / Della Cioppa, Lorenzo; Tartaglione, Michela; Pascarella, Annalisa; Pitolli, Francesca. - In: INVERSE PROBLEMS. - ISSN 0266-5611. - 40:(2024). [10.1088/1361-6420/ad14a1]

Solution of the EEG inverse problem by random dipole sampling

Della Cioppa, Lorenzo;Tartaglione, Michela;Pascarella, Annalisa;Pitolli, Francesca
2024

Abstract

Electroencephalography (EEG) source imaging aims to reconstruct brain activity maps from the neuroelectric potential difference measured on the skull. To obtain the brain activity map, we need to solve an ill-posed and ill-conditioned inverse problem that requires regularization techniques to make the solution viable. When dealing with real-time applications, dimensionality reduction techniques can be used to reduce the computational load required to evaluate the numerical solution of the EEG inverse problem. To this end, in this paper we use the random dipole sampling method, in which a Monte Carlo technique is used to reduce the number of neural sources. This is equivalent to reducing the number of the unknowns in the inverse problem and can be seen as a first regularization step. Then, we solve the reduced EEG inverse problem with two popular inversion methods, the weighted Minimum Norm Estimate (wMNE) and the standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA). The main result of this paper is the error estimates of the reconstructed activity map obtained with the randomized version of wMNE and sLORETA. Numerical experiments on synthetic EEG data demonstrate the effectiveness of the random dipole sampling method.
2024
EEG imaging, underdetermined inverse problem, random sampling, inversion method, wMNE, sLORETA
01 Pubblicazione su rivista::01a Articolo in rivista
Solution of the EEG inverse problem by random dipole sampling / Della Cioppa, Lorenzo; Tartaglione, Michela; Pascarella, Annalisa; Pitolli, Francesca. - In: INVERSE PROBLEMS. - ISSN 0266-5611. - 40:(2024). [10.1088/1361-6420/ad14a1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1696446
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