Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning in Brain-Computer Interfaces   The performance of brain-computer interfaces (BCIs) improves with the amount
of available training data, the statistical distribution of this data, however,
varies across subjects as well as across sessions within individual subjects,
limiting the transferability of training data or trained models between them.
In this article, we review current transfer learning techniques in BCIs that
exploit shared structure between training data of multiple subjects and/or
sessions to increase performance. We then present a framework for transfer
learning in the context of BCIs that can be applied to any arbitrary feature
space, as well as a novel regression estimation method that is specifically
designed for the structure of a system based on the electroencephalogram (EEG).
We demonstrate the utility of our framework and method on subject-to-subject
transfer in a motor-imagery paradigm as well as on session-to-session transfer
in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that
it is able to outperform other comparable methods on an identical dataset.

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The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
3
The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol