Descrizione del progetto
Verso interfacce cervello-computer avanzate
Le interfacce cervello-computer (BCI, brain–computer interface) possono scavalcare il sistema muscolo-scheletrico, fornendo assistenza alle persone paralizzate, in termini di controllo e comunicazione. Tuttavia, nonostante la loro applicazione nella riabilitazione neuromotoria, la precisione del feedback sensoriale è tuttora altamente variabile, il che ne limita l’utilizzo nella vita quotidiana. Gli scienziati del progetto BCINET, finanziato dall’UE, propongono di affrontare tale problema grazie all’impiego di una nuova generazione di BCI che non si affidano solamente ai dati provenienti da determinate regioni cerebrali, ma integrano inoltre le informazioni della rete cerebrale dell’utente. Con l’obiettivo di migliorare l’architettura e la precisione delle BCI, i ricercatori utilizzeranno una combinazione di metodi sperimentali e di neuroimaging nell’ambito di un moderno quadro computazionale per studiare le dinamiche del cervello. Oltre a occuparsi del perfezionamento delle BCI, il progetto ha le potenzialità per fornire soluzioni intese al ripristino motorio in seguito a ictus.
Obiettivo
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.
Campo scientifico
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-COG - Consolidator GrantIstituzione ospitante
78153 Le Chesnay Cedex
Francia