# Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

## TL;DR

This study investigates how distributed brain networks support learning in motor imagery-based brain-computer interfaces using multimodal network analysis and matrix decomposition, revealing key subgraphs linked to successful control and attention modulation.

## Contribution

It introduces a novel multimodal network approach with non-negative matrix factorization to identify and analyze brain subgraphs related to BCI learning, highlighting the role of attention-related networks.

## Key findings

- Successful learners show a specific subgraph linked to attention modulation.
- Subgraph expression correlates with performance over time.
- Network control theory supports the importance of attention-related subgraphs.

## Abstract

Motor imagery-based brain-computer interfaces (BCIs) use an individuals ability to volitionally modulate localized brain activity as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Previous research suggests that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using MEG. Specifically, we employ a minimally constrained matrix decomposition method (non-negative matrix factorization) to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention.