Network neuroscience for optimizing brain–computer interfaces
Résumé
Human-machine interactions are being increasingly explored to create alternative waysof communication and to improve our daily life. Based on a classification of the user’sintention from the user’s underlying neural activity, brain-computer interfaces (BCIs)allow direct interactions with the external environment while bypassing the traditionaleffector of the musculoskeletal system. Despite the enormous potential of BCIs, thereare still a number of challenges that limit their societal impact, ranging from the correctdecoding of a human’s thoughts, to the application of effective learning strategies. Despiteseveral important engineering advances, the basic neuroscience behind these challengesremains poorly explored. Indeed, BCIs involve complex dynamic changes related toneural plasticity at a diverse range of spatiotemporal scales. One promising antidote tothis complexity lies in network science, which provides a natural language in which tomodel the organizational principles of brain architecture and function as manifest in itsinterconnectivity. Here, we briefly review the main limitations currently affecting BCIs,and we offer our perspective on how they can be addressed by means of network theoreticapproaches. We posit that the emerging field of network neuroscience will prove to bean effective tool to unlock human-machine interactions.
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