Magnetoencephalographic source localization and reconstruction via deep learning
Within this manuscript a deep learning algorithm designed to achieve both spatial and temporal source reconstruction based on signals captured by MEG devices is introduced. Brain signal estimation at source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditiona...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1578473/full |
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| author | Stefano Franceschini Michele Ambrosanio Maria Maddalena Autorino Sohail Maqsood Fabio Baselice |
| author_facet | Stefano Franceschini Michele Ambrosanio Maria Maddalena Autorino Sohail Maqsood Fabio Baselice |
| author_sort | Stefano Franceschini |
| collection | DOAJ |
| description | Within this manuscript a deep learning algorithm designed to achieve both spatial and temporal source reconstruction based on signals captured by MEG devices is introduced. Brain signal estimation at source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms offer excellent temporal resolution but are limited in spatial resolution due to the inherent ill-posed nature of the problem. Nevertheless, many applications require precise localization of pathological tissues to provide reliable information for clinicians. In this context, deep learning solutions emerge as promising candidates for high resolution signals estimations. The proposed approach, termed “Deep-MEG,” employs a hybrid neural network architecture capable of extracting both temporal and spatial information from signals captured by MEG sensors. The algorithm is capable to handling the entire brain and, therefore, is not limited to cortical sources imaging. To validate its efficacy, the Authors conducted simulations involving multiple active sources using a realistic forward model, and subsequently compared the results with those obtained using various state-of-the-art reconstruction algorithms. Finally Deep-MEG has been tested also with real MEG data. |
| format | Article |
| id | doaj-art-0ff7a43e5bb6422f99665e2f8fba6e22 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-0ff7a43e5bb6422f99665e2f8fba6e222025-08-20T03:36:57ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-07-011910.3389/fnins.2025.15784731578473Magnetoencephalographic source localization and reconstruction via deep learningStefano Franceschini0Michele Ambrosanio1Maria Maddalena Autorino2Sohail Maqsood3Fabio Baselice4Department of Engineering, University of Naples Parthenope, Naples, ItalyDepartment of Economics, Law, Cybersecurity and Sports Sciences (DiSEGIM), University of Naples Parthenope, Naples, ItalyDepartment of Engineering, University of Naples Parthenope, Naples, ItalyDepartment of Engineering, University of Naples Parthenope, Naples, ItalyDepartment of Engineering, University of Naples Parthenope, Naples, ItalyWithin this manuscript a deep learning algorithm designed to achieve both spatial and temporal source reconstruction based on signals captured by MEG devices is introduced. Brain signal estimation at source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms offer excellent temporal resolution but are limited in spatial resolution due to the inherent ill-posed nature of the problem. Nevertheless, many applications require precise localization of pathological tissues to provide reliable information for clinicians. In this context, deep learning solutions emerge as promising candidates for high resolution signals estimations. The proposed approach, termed “Deep-MEG,” employs a hybrid neural network architecture capable of extracting both temporal and spatial information from signals captured by MEG sensors. The algorithm is capable to handling the entire brain and, therefore, is not limited to cortical sources imaging. To validate its efficacy, the Authors conducted simulations involving multiple active sources using a realistic forward model, and subsequently compared the results with those obtained using various state-of-the-art reconstruction algorithms. Finally Deep-MEG has been tested also with real MEG data.https://www.frontiersin.org/articles/10.3389/fnins.2025.1578473/fullbeamformingbrain signal estimationbrain source reconstructionneural networksmagnetoencephalography |
| spellingShingle | Stefano Franceschini Michele Ambrosanio Maria Maddalena Autorino Sohail Maqsood Fabio Baselice Magnetoencephalographic source localization and reconstruction via deep learning Frontiers in Neuroscience beamforming brain signal estimation brain source reconstruction neural networks magnetoencephalography |
| title | Magnetoencephalographic source localization and reconstruction via deep learning |
| title_full | Magnetoencephalographic source localization and reconstruction via deep learning |
| title_fullStr | Magnetoencephalographic source localization and reconstruction via deep learning |
| title_full_unstemmed | Magnetoencephalographic source localization and reconstruction via deep learning |
| title_short | Magnetoencephalographic source localization and reconstruction via deep learning |
| title_sort | magnetoencephalographic source localization and reconstruction via deep learning |
| topic | beamforming brain signal estimation brain source reconstruction neural networks magnetoencephalography |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1578473/full |
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