Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning in brain-computer interface control evidenced by joint
  decomposition of brain and behavior   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.

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The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
2
The categories of this paper are: math
3
The categories of this paper are: physics