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

TL;DR

This study investigates how BCI training induces neural reorganization in brain networks by integrating EEG and MEG data, revealing changes in sensory and cognitive areas that correlate with BCI performance.

Contribution

It introduces a multilayer network approach combining EEG and MEG data to analyze neural changes during BCI learning, highlighting potential neural markers of BCI proficiency.

Findings

Increased integration of somatosensory areas in alpha band during training

Decreased integration of visual and working memory areas in beta band

Multilayer network properties in alpha2 band correlate with BCI scores

Abstract

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.