Publications by authors named "R Le Bouquin Jeannes"
Article Synopsis
- The paper addresses the challenge of accurately identifying the location and electrical activity of brain sources in epilepsy using EEG recordings due to the complexity of the problem.
- A new approach using simulation-driven deep learning is proposed, which incorporates a patient-specific model trained on high-resolution EEG simulations and utilizes neural networks to analyze spatial and temporal features.
- The performance of this method shows significant improvements in dipole localization accuracy compared to existing deep learning and classical techniques, tested on both synthetic and real EEG data from patients with drug-resistant epilepsy.
Purpose: This study attempted to determine whether there is a localized effect of GSM (Global System for Mobile communications) microwaves by studying the Auditory Evoked Potentials (AEP) recorded at the scalp of nine healthy subjects and six epileptic patients.
Materials And Methods: We determined the influence of GSM RadioFrequency (RF) on parameters characterizing the AEP in time or/and frequency domains. A parameter selection method using SVM (Support Vector Machines)-based criteria allowed us to estimate those most altered by the radiofrequencies.
Two computational models replicating amplitude-modulation encoding in the inferior colliculus (IC) are presented and compared. Neurons in this nucleus are modeled as point neurons using Mc Gregor equations, and receive depolarizing currents from action potentials delivered by stellate cells (chopper units) in the cochlear nucleus (CN). Stellate cells are modeled using modified Hodgkin-Huxley equations and receive inputs from a peripheral auditory model.
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