<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For these reasons the random sampling method is particularly attractive in real-time MEG applications.</p></abstract>

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For these reasons the random sampling method is particularly attractive in real-time MEG applications.

An inversion method based on random sampling for real-time MEG neuroimaging

Dipartimento di Scienze di Base e Applicate per l’Ingegneria

Università degli Studi di Roma La Sapienza

Communications in Applied and Industrial Mathematics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"An inversion method based on random sampling for real-time MEG neuroimaging"}

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize...

An inversion method based on random sampling for real-time MEG neuroimaging

Online veröffentlicht: 11. Mai 2019

Seitenbereich: 25 - 34

Eingereicht: 09. Nov. 2016

Akzeptiert: 14. Nov. 2017

DOI: https://doi.org/10.2478/caim-2019-0004

Schlüsselwörter
neuroimaging, magnetoencephalography, source localization, inverse problem, random sampling

© 2019 Annalisa Pascarella et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

An inversion method based on random sampling for real-time MEG neuroimaging

Online veröffentlicht: 11. Mai 2019

Seitenbereich: 25 - 34

Eingereicht: 09. Nov. 2016

Akzeptiert: 14. Nov. 2017

DOI: https://doi.org/10.2478/caim-2019-0004

Schlüsselwörterneuroimaging, magnetoencephalography, source localization, inverse problem, random sampling

© 2019 Annalisa Pascarella et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
neuroimaging, magnetoencephalography, source localization, inverse problem, random sampling