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Brain-computer interfaces (BCIs) constitute a promising tool forcommunication and control. However, mastering non-invasive closed-loop systemsremains a learned skill that is difficult to develop for a non-negligibleproportion of users. The involved learning process induces neural changesassociated with a brain network reorganization that remains poorly understood.To address this inter-subject variability, we adopted a multilayer approach tointegrate brain network properties from electroencephalographic (EEG) andmagnetoencephalographic (MEG) data resulting from a four-session BCI trainingprogram followed by a group of healthy subjects. Our method gives access to thecontribution of each layer to multilayer network that tends to be equal withtime. We show that regardless the chosen modality, a progressive increase inthe integration of somatosensory areas in the alpha band was paralleled by adecrease of the integration of visual processing and working memory areas inthe beta band. Notably, only brain network properties in multilayer networkcorrelated with future BCI scores in the alpha2 band: positively insomatosensory and decision-making related areas and negatively in associativeareas. Our findings cast new light on neural processes underlying BCI training.Integrating multimodal brain network properties provides new information thatcorrelates with behavioral performance and could be considered as a potentialmarker of BCI learning.

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