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...
Saved in:
| Main Authors: | Stefano Franceschini, Michele Ambrosanio, Maria Maddalena Autorino, Sohail Maqsood, Fabio Baselice |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1578473/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Beamforming of ictal MEG aiding subtle focal cortical dysplasia localization
by: Natascha C. da Fonseca, et al.
Published: (2025-06-01) -
Structured noise champagne: an empirical Bayesian algorithm for electromagnetic brain imaging with structured noise
by: Sanjay Ghosh, et al.
Published: (2025-04-01) -
Visual sensory discrimination of threatening stimuli presenting different durations: A magnetoencephalographic and behavioral study
by: Luis Carretié, et al.
Published: (2025-04-01) -
Decoding motor execution and motor imagery from EEG with deep learning and source localization
by: Sina Makhdoomi Kaviri, et al.
Published: (2025-06-01) -
Differential late-stage face processing in autism: a magnetoencephalographic study of fusiform gyrus activation
by: Darko Sarovic, et al.
Published: (2024-12-01)