Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Optimization of Stimulation Speed in an Auditory Brain-Computer
  Interface under Time Constraints   The decoding of brain signals recorded via, e.g., an electroencephalogram,
using machine learning is key to brain-computer interfaces (BCIs). Stimulation
parameters or other experimental settings of the BCI protocol typically are
chosen according to the literature. The decoding performance directly depends
on the choice of parameters, as they influence the elicited brain signals and
optimal parameters are subject-dependent. Thus a fast and automated selection
procedure for experimental parameters could greatly improve the usability of
BCIs.
  We evaluate a standalone random search and a combined Bayesian optimization
with random search in a closed-loop auditory event-related potential protocol.
We aimed at finding the individually best stimulation speed -- also known as
stimulus onset asynchrony (SOA) -- that maximizes the classification
performance of a regularized linear discriminant analysis. To make the Bayesian
optimization feasible under noise and the time pressure posed by an online BCI
experiment, we first used offline simulations to initialize and constrain the
internal optimization model. Then we evaluated our approach online with 13
healthy subjects.
  We could show that for 8 out of 13 subjects, the proposed approach using
Bayesian optimization succeeded to select the individually optimal SOA out of
multiple evaluated SOA values. Our data suggests, however, that subjects were
influenced to very different degrees by the SOA parameter. This makes the
automatic parameter selection infeasible for subjects where the influence is
limited.
  Our work proposes an approach to exploit the benefits of individualized
experimental protocols and evaluated it in an auditory BCI. When applied to
other experimental parameters our approach could enhance the usability of BCI
for different target groups -- specifically if an individual disease progress
may prevent the use of standard parameters.

22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex