BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks - Archive ouverte HAL Access content directly
Journal Articles Journal of Neural Engineering Year : 2021

BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

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Abstract

Objective: Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. Approach: To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. Main results: We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α 2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Significance: Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
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Dates and versions

hal-03171591 , version 1 (17-03-2021)

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Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, et al.. BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks. Journal of Neural Engineering, 2021, 18 (5), pp.056002. ⟨10.1088/1741-2552/abef39⟩. ⟨hal-03171591⟩
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