Periodic Reporting for period 2 - BCINET (Non-invasive decoding of brain communication patterns to ease motor restoration after stroke)
Berichtszeitraum: 2022-04-01 bis 2023-09-30
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.
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.
From the beginning of the project, we have advanced on the following scientific and technological research lines:
Brain connectivity. A major achievement was to demonstrate that motor imagery-based tasks elicit stable brain network changes that can be used to improve BCI performance (Cattai et al, 2021). This is the first proof-of-concept of a brain network-based BCI.
Topological duality. We unveiled the topological duality of multilayer networks to better characterize different real-world multilayer networks, including social, infrastructure and biological systems. Taken together, these results unveil previously unappreciated hidden properties of multilayer networks that can be further developed to study the structure and dynamics of complex interconnected systems.
Network controllability. We developed a new framework that combines concepts from graph signal processing and modern network science to control complex networks. Specifically, we derived low-dimensional controllability metrics to identify the main driver areas in brain networks. Our results offer a novel grounded solution to deal with network controllability in real-life applications and provide new insights into the causal interactions of the human connectome.
Temporal network. We introduced a novel formalism that mimics evolutionary dynamics to model temporal network processes, such as the brain wiring development. Our framework can be readily used to study the temporal dynamics of complex systems arising from the interplay of the stochastic configuration variability and the state-dependent optimization of an objective function, such as those occurring during BCI learning.
BCI software. A major achievement is the realization of an interactive plug-in software to ease the use of existing BCI platforms. The software is intended to be open-source and allows unprecedented flexibility in the parametrization of the settings. The software also allowed to integrated the network methods we develop in the framework of the ERC BCINET. This is crucial to let the experimenter interactively personalize BCI settings off-line and on-line, especially in clinical settings.
BCI platform. We developed and successfully installed a multimodal BCI platform within the Neuroimaging core-facility of the Paris Brain Institute. The platform includes a number of cutting-edge technological solutions (eye-tracker, interactive screen, robotic device) enabling a more ecological experimental scenario. We used the BCI platform to explore the effects of different types of motor imagery on the BCI performance over time.
Brain connectivity. A major achievement was to demonstrate that motor imagery-based tasks elicit stable brain network changes that can be used to improve BCI performance (Cattai et al, 2021). This is the first proof-of-concept of a brain network-based BCI.
Topological duality. We unveiled the topological duality of multilayer networks to better characterize different real-world multilayer networks, including social, infrastructure and biological systems. Taken together, these results unveil previously unappreciated hidden properties of multilayer networks that can be further developed to study the structure and dynamics of complex interconnected systems.
Network controllability. We developed a new framework that combines concepts from graph signal processing and modern network science to control complex networks. Specifically, we derived low-dimensional controllability metrics to identify the main driver areas in brain networks. Our results offer a novel grounded solution to deal with network controllability in real-life applications and provide new insights into the causal interactions of the human connectome.
Temporal network. We introduced a novel formalism that mimics evolutionary dynamics to model temporal network processes, such as the brain wiring development. Our framework can be readily used to study the temporal dynamics of complex systems arising from the interplay of the stochastic configuration variability and the state-dependent optimization of an objective function, such as those occurring during BCI learning.
BCI software. A major achievement is the realization of an interactive plug-in software to ease the use of existing BCI platforms. The software is intended to be open-source and allows unprecedented flexibility in the parametrization of the settings. The software also allowed to integrated the network methods we develop in the framework of the ERC BCINET. This is crucial to let the experimenter interactively personalize BCI settings off-line and on-line, especially in clinical settings.
BCI platform. We developed and successfully installed a multimodal BCI platform within the Neuroimaging core-facility of the Paris Brain Institute. The platform includes a number of cutting-edge technological solutions (eye-tracker, interactive screen, robotic device) enabling a more ecological experimental scenario. We used the BCI platform to explore the effects of different types of motor imagery on the BCI performance over time.
So far, the existence of a topological duality in multilayer networks represents the most disruptive discovery in terms of potential broader impact and spots out a hidden side of brain network analysis that can be extremely relevant for decoding brain states. This theoretical study offers an advancement that goes beyond the state of the art in the field of complex systems. Results are striking as they quantify how much information we have lost by neglecting that aspect of multilayer networks.