V
Valérie Louis-Dorr
Researcher at University of Lorraine
Publications - 47
Citations - 510
Valérie Louis-Dorr is an academic researcher from University of Lorraine. The author has contributed to research in topics: Wavelet & Blind signal separation. The author has an hindex of 11, co-authored 45 publications receiving 394 citations. Previous affiliations of Valérie Louis-Dorr include Centre national de la recherche scientifique.
Papers
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Journal ArticleDOI
Evaluating dipolar source localization feasibility from intracerebral SEEG recordings
Vairis Caune,Radu Ranta,Radu Ranta,S. Le Cam,Janis Hofmanis,Louis Maillard,Laurent Koessler,Valérie Louis-Dorr +7 more
TL;DR: A straightforward approach based on an equivalent current dipole model for the source and on simple analytical volume conduction models yields sufficiently precise solutions of the localization problem, showing electrical source imaging using SEEG signals is a promising tool for distant brain source investigation and might be used as a complement to routine visual interpretations.
Journal ArticleDOI
Digestive Activity Evaluation by Multichannel Abdominal Sounds Analysis
TL;DR: It is shown that the abdominal regions of healthy volunteers present statistically significant phonoenterographic characteristics, which evolve differently during the normal digestion, and is usable in further studies as a comparison term with other normal or pathological conditions.
Journal ArticleDOI
Combined SEEG and source localisation study of temporal lobe schizencephaly and polymicrogyria
Louis Maillard,Laurent Koessler,Sophie Colnat-Coulbois,Jean-Pierre Vignal,Valérie Louis-Dorr,Pierre-Yves Marie,Hervé Vespignani +6 more
TL;DR: The purpose was to combine and compare dipole source imaging technique and Stereo-EEG (SEEG) technique in determining the irritative and epileptogenic zones in a case of type 1 schizencephaly.
Journal ArticleDOI
Retinal blood vessels segmentation using classical edge detection filters and the neural network
TL;DR: This paper proposes a new method for the segmentation of blood vessels in retinal photographs, based on classical edge detection filters and artificial neural networks, which is a suitable tool for automated retinal image analysis.
Proceedings ArticleDOI
EEG Ocular Artefacts and Noise Removal
TL;DR: The goal of this paper is to compare several combinations of wavelet denoising (WD) and independent component analysis (ICA) algorithms for noise and artefacts removal on simulated EEG, using different evaluation criteria.