F
François-Benoît Vialatte
Researcher at PSL Research University
Publications - 33
Citations - 1514
François-Benoît Vialatte is an academic researcher from PSL Research University. The author has contributed to research in topics: Electroencephalography & Neurofeedback. The author has an hindex of 12, co-authored 33 publications receiving 1288 citations. Previous affiliations of François-Benoît Vialatte include Centre national de la recherche scientifique & ESPCI ParisTech.
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Journal ArticleDOI
Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.
TL;DR: The steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation are investigated and future research directions related to basic and applied aspects of SSVEPs are outlined.
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Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?
Justin Dauwels,K.N. Srinivasan,M. Ramasubba Reddy,Toshimitsu Musha,François-Benoît Vialatte,Charles Latchoumane,Jaeseung Jeong,Andrzej Cichocki +7 more
TL;DR: It is shown that strong correlation between slowing and loss of complexity is observed in two independent EEG datasets, and relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects.
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A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback.
TL;DR: This manuscript presents an integrative psychoengineering model of the feedback learning processes, and provides new guidelines for the efficient design of biofeedback and neurofeedback protocols.
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Neurofeedback: One of today's techniques in psychiatry?
Martijn Arns,J-M Batail,Stéphanie Bioulac,Marco Congedo,C. Daudet,Dominique Drapier,Thomas Fovet,Renaud Jardri,M Le-Van-Quyen,Fabien Lotte,David M. A. Mehler,J A Micoulaud-Franchi,J A Micoulaud-Franchi,Diane Purper-Ouakil,François-Benoît Vialatte +14 more
TL;DR: Results show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, and the level of evidence of fMRI neurofeedback remains too weak, for now, to justify clinical use.
Journal ArticleDOI
A hybrid feature selection approach for the early diagnosis of Alzheimer's disease
Esteve Gallego-Jutglà,Jordi Solé-Casals,François-Benoît Vialatte,Mohamed Elgendi,Mohamed Elgendi,Andrzej Cichocki,Justin Dauwels +6 more
TL;DR: The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status, and explores the standardization of features for MCI and Mild AD data sets with promising results.