LOTTE , Fabien

National audience

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview...

International audience

Brain–computer interfaces (BCI) are devices which measure brain activity and translate it into messages or commands, thereby opening up many investigation and application possibilities. This book provides keys for understanding and designing these multi-disciplinary interfaces, which require many fields of expertise such as neuroscience,...

Les interfaces cerveau-ordinateur (Brain-Computer Interfaces, BCI) sont des dispositifs qui mesurent l'activité cérébrale et la convertissent en messages ou commandes, offrant ainsi de nombreuses possibilités d'investigation.Ce second volume, Technologie et applications, permet d'appréhender le potentiel mais également les limites des BCI. Il...

International audience

Brain–computer interfaces (BCI) are devices which measure brain activity and translate it into messages or commands, thereby opening up many possibilities for investigation and application. This book provides keys for understanding and designing these multi-disciplinary interfaces, which require many fields of expertise such as neuroscience,...

International audience

Les interfaces cerveau-ordinateur (Brain-Computer Interfaces, BCI) sont des dispositifs qui mesurent l'activité cérébrale et la convertissent en messages ou commandes, offrant ainsi de nombreuses possibilités d'investigation et d'application.Pluridisciplinaire, cet ouvrage donne les clés pour comprendre et concevoir ces interfaces qui font...

International audience

International audience

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and...

Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been...

EEG-fMRI-neurofeedback(NF) has been introduced for the first time by Zotev et al [1]. The authors hypothesized that bimodal EEG-fMRI-NF could be more efficient than unimodal EEG-NF or fMRI-NF performed alone. A recent study identified the fMRI signature of motor imagery during EEG-NF [3]. However to our knowledge EEG-fMRI-NF, EEG-NF and fMRI-NF...

Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain activity by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly electroencephalography (EEG) or functional magnetic resonance imaging (fMRI)....

International audience