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Poster Année : 2023

Informing motor imagery-based brain-computer interface via neuronal avalanches

Résumé

Brain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive BCI systems remains a learned skill difficult to develop for a non-negligible proportion of users. Even though similarities have been shown between MI-based BCI learning and motor sequence learning our understanding of the dynamical processes, and their reflection on brain signals during BCI performance is still incomplete. In particular, whole-brain functional imaging is dominated by a 'bursty' dynamics, "neuronal avalanches", with fast, fat-tailed distributed, aperiodic perturbations spreading across the whole brain. Neuronal avalanches evolve over a manifold during resting-state, generating a rich functional connectivity dynamics. In this work, we evaluated to which extent neuronal avalanches can be used as a tool to differentiate mental states in the context of BCI experiments.
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Dates et versions

hal-04140120 , version 1 (24-06-2023)

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  • HAL Id : hal-04140120 , version 1

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Pierpaolo Sorrentino, Marie-Constance Corsi, Denis P Schwartz, Nathalie George, Ari E Kahn, et al.. Informing motor imagery-based brain-computer interface via neuronal avalanches. IEEE International Symposium on Biomedical Imaging (ISBI) 2023, Apr 2023, Cartagena, Colombia. . ⟨hal-04140120⟩
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