M/EEG networks integration to elicit patterns of motor imagery-based BCI training - Inria - Institut national de recherche en sciences et technologies du numérique
Poster Communications Year : 2022

M/EEG networks integration to elicit patterns of motor imagery-based BCI training

Abstract

Non-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users [1]. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in multimodal network recruitment. We hypothesized that integrating information from EEG and MEG data, show a better description of the core-periphery changes occurring during a motor imagery-based BCI training. Such an enriched description could reveal fresh insights into learning processes that are difficult to observe at the signle layer level and eventually improve the prediction of future BCI performance.multimodal brain network properties could be considered as a potential marker of BCI learning.
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Dates and versions

hal-03695420 , version 1 (27-07-2022)

Identifiers

  • HAL Id : hal-03695420 , version 1

Cite

Marie-Constance Corsi, Mario Chavez, Denis P Schwartz, Nathalie George, Laurent Hugueville, et al.. M/EEG networks integration to elicit patterns of motor imagery-based BCI training. FENS Forum 2022, Jul 2022, Paris, France. ⟨hal-03695420⟩
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