Description du projet
Vers des interfaces cerveau-ordinateur avancées
Les interfaces cerveau-ordinateur (BCI pour «brain–computer interfaces») peuvent contourner le système musculosquelettique, aidant ainsi les personnes paralysées dans les tâches de contrôle et de communication. Toutefois, malgré leur application dans la rééducation neuromotrice, la précision du retour sensoriel reste très variable, limitant ainsi leur utilisation quotidienne. Les chercheurs du projet BCINET, financé par l’UE, proposent de relever ce défi par le biais d’une nouvelle génération de BCI qui ne dépende pas uniquement des données issues de régions cérébrales sélectionnées, mais qui intègre des informations sur le réseau cérébral de l’utilisateur. En combinant neuro-imagerie et méthodes expérimentales dans un cadre informatique moderne, ils étudieront la dynamique cérébrale afin d’améliorer l’architecture et la précision des BCI. En plus de peaufiner les BCI, le projet pourrait initier des solutions pour restaurer les capacités motrices à la suite d’un accident vasculaire cérébral.
Objectif
Human-computer interfaces are increasingly explored to facilitate interaction with the external world. Brain-computer interfaces (BCIs), bypassing the skeletomuscular system, are particularly promising for assisting paralyzed people in control and communication, but also for boosting neuromotor rehabilitation.
Despite their potential, the societal impact of BCIs is dramatically limited by the poor usability in real-life applications. While many solutions have been proposed - from the identification of the best classification algorithm to the type of sensory feedback - the accuracy is still highly variable across subjects and BCIs cannot be used by everyone. Critically, these approaches have implicitly assumed that the user’s intent could be decoded by examining the activity of single brain areas. Today, we know that this is not true as the brain functioning essentially depends on a complex network of interactions between differently specialized areas.
The grand challenge of this project is to develop a novel generation of BCIs that integrate the user’s brain network information for enhancing accuracy and usability. Based on this approach, we will experiment innovative BCI prototypes to restore the lost motor functions in patients suffering from stroke.
This project relies on a unifying framework that analyses and models brain networks by means of analytical tools derived from graph theory and complex systems science. By recruiting diverse neuroimaging and experimental methods, within a modern computational framework, we aim to i) identify new control features for enhancing BCI accuracy, ii) study the brain dynamics of human learning for improving adaptive BCI architectures, and iii) optimize brain stimulation techniques for boosting BCI skill acquisition.
This project can significantly improve BCI usability as well as determining how brain lesions compromise brain functioning and which solutions are most effective to unlock motor restoration after stroke.
Champ scientifique
Programme(s)
Régime de financement
ERC-COG - Consolidator GrantInstitution d’accueil
78153 Le Chesnay Cedex
France