2018-00578 - [CORDIS2018-EPIONE] PhD: Statistical Dimension Reduction in Non-Linear Manifolds for Brain Shape Analysis, Connectomics & Brain-Computer Interfaces
ثبت نشده
چکیده
Le centre Inria Sophia Antipolis Méditerranée compte 37 équipes de recherche, ainsi que 9 services d’appui à la recherche. Le personnel du centre (600 personnes environ dont 400 salariés Inria) est composé de scientifiques de différentes nationalités (250 personnes étrangères sur 50 nationalités), d’Ingénieurs, de Techniciens et d’Administratifs. 1/3 du personnel est fonctionnaire, les autres sont contractuels. La majorité des équipes de recherche du centre sont localisées à Sophia Antipolis et Nice dans les Alpes-Maritimes. Six équipes sont implantées à Montpellier et une équipe est hébergée par le département d'informatique de l'université de Bologne en Italie. Le Centre est membre de la Communauté d’Université et d’Établissement (ComUE) « Université Côte d’Azur (UCA) ».
منابع مشابه
Statistical Dimension Reduction in Non-Linear Manifolds for Brain Shape Analysis, Connectomics & Brain-Computer Interfaces
In many applications, data belong to non-linear sub-manifolds of a high dimensional space. The natural invariance properties of the space in which the data live often encode informative priors that turn out to be key features to improve the results of analyses. This is the case in Computational Anatomy (CA), Brain Computer Interfaces (BCI) and Brain Connectomics where data naturally belong to s...
متن کاملSelecting and Extracting Effective Features of SSVEP-based Brain-Computer Interface
User interfaces are always one of the most important applied and study fields of information technology. The development and expansion of cognitive science studies and functionalization of its tools such as BCI1, as well as popularization of methods such as SSVEP2 to stimulate brain waves, have led to using these techniques every day, especially in appropriate solutions for physically and menta...
متن کاملApplying Genetic Algorithm to EEG Signals for Feature Reduction in Mental Task Classification
Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states. Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...
متن کاملComparison of Different Linear Filter Design Methods for Handling Ocular Artifacts in Brain Computer Interface System
Brain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI systems is to provide better life style for physically-challenged people which are suffered from cerebral palsy, amyotrophic l...
متن کاملEEG Based Brain Computer Interface Hand Grasp Control: Feature Extraction Method MTCSP
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
متن کامل