Descripción del proyecto
Hacia unas interfaces cerebro-ordenador avanzadas
Las interfaces cerebro-ordenador (ICO) pueden engañar al sistema musculoesquelético y ayudar a las personas paralíticas en el control y la comunicación. Sin embargo, a pesar de su aplicación en la rehabilitación neuromotora, la precisión de la respuesta sensorial todavía es muy variable, lo cual limita su uso en la vida cotidiana. Los científicos del proyecto BCINET, financiado con fondos europeos, proponen abordar este problema a través de una nueva generación de ICO que no se base únicamente en los datos de determinadas regiones del encéfalo, sino que integre la información de la red encefálica del usuario. Mediante una combinación de neuroimagenología y métodos experimentales dentro de un moderno entorno computacional, estudiarán la dinámica del encéfalo para mejorar la arquitectura y la precisión de las ICO. Aparte de perfeccionar las ICO, el proyecto podría revelar soluciones para la recuperación motora tras un ictus.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
78153 Le Chesnay Cedex
Francia