Project description
Towards advanced brain–computer interfaces
Brain–computer interfaces (BCIs) can bypass the skeletomuscular system, assisting paralysed people in control and communication. However, despite their application in neuromotor rehabilitation, the accuracy of sensory feedback is still highly variable, limiting their use in everyday life. Scientists of the EU-funded BCINET project propose to address this issue through a novel generation of BCIs that do not solely rely on data from selected brain regions but integrate the user’s brain network information. Using a combination of neuroimaging and experimental methods within a modern computational framework, they will study brain dynamics to improve BCI architecture and accuracy. Apart from refining BCIs, the project has the potential to unveil solutions for motor restoration after stroke.
Objective
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.
Fields of science
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
78153 Le Chesnay Cedex
France