  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.
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. We show that regardless the chosen modality, a progressive increase in
the integration of somatosensory areas in the alpha band was paralleled by a
decrease of the integration of visual processing and working memory areas in
the beta band. Notably, only brain network properties in multilayer network
correlated with future BCI scores in the alpha2 band: positively in
somatosensory and decision-making related areas and negatively in associative
areas. 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.
