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BCINET

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Non invasive decoding of brain communication patterns to ease motor restoration...

Non invasive decoding of brain communication patterns to ease motor restoration after stroke 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 peop... ver más

30/09/2027

INRIA

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-04-08

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2019-COG: ERC Consolidator Grant

I+D

Cerrada hace 5 años

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3 Participantes

INRIA

1.69M€ | Lider

TRUMPLER ESPAÑOLA,

681.76K€ | Participante

MINISTERE DE L'ENSEIGNEMENT...

309.50K€ | Participante

Últimas noticias

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

01-12-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

01-12-2024: AEI En las últimas 48 horas el Organismo AEI ha otorgado 23 concesiones

Duración del proyecto: 89 meses Fecha Inicio: 2020-04-08
Fecha Fin: 2027-09-30

Presupuesto del proyecto 2M€

Descripción del proyecto 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.

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