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BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
M-C Corsi1,2, M Chavez3, D Schwartz4
1Inria Paris, Aramis project-team, F-75013 Paris, France.
Journal of Neural Engineering
|March 16, 2021
Summary
Brain-computer interfaces (BCIs) require learning and cause brain network changes. Multilayer network analysis reveals how somatosensory and visual areas adapt, offering insights into BCI learning and performance prediction.
Area of Science:
- Neuroscience
- Biomedical Engineering
Background:
- Brain-computer interfaces (BCIs) offer communication and control but present learning challenges for users.
- The neural mechanisms and brain network reorganization during BCI learning are not fully understood.
Purpose of the Study:
- To investigate inter-subject variability in BCI learning by analyzing brain network properties.
- To understand neural changes associated with mastering non-invasive closed-loop BCI systems.
Main Methods:
- Utilized a multilayer approach integrating electroencephalography (EEG) and magnetoencephalography (MEG) data.
- Analyzed brain network properties over a four-session BCI training program in healthy subjects.
Main Results:
- Observed increased integration in somatosensory areas (α band) and decreased integration in visual/working memory areas (β band) with training.
- Multilayer network properties in the α2 band correlated with BCI performance: positively with somatosensory/decision-making areas, negatively with associative areas.
Conclusions:
- BCI training induces specific, measurable changes in brain network integration across different frequency bands.
- Multilayer brain network properties show potential as biomarkers for BCI learning and predicting behavioral performance.
