The online optimization of brain-computer interface stimulus parameters, a simulation
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2023-08-20
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en
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Abstract
A brain-computer interface operates by presenting stimuli to elicit brain responses that can be
acted upon. As the discriminability of these responses is subject to the stimulus characteristics,
the presentation of the optimal stimuli could improve the performance of the interface.
Unfortunately, these optima are subject-specific. Furthermore, the optimization of said stimuli
is complicated by the potentially high level of non-independent and identically distributed
noise that comes with the recorded responses. The aim of this thesis is to demonstrate that
these challenges can be alleviated by introducing the optimization process with heteroskedatic
modelling and the replication of existing designs. To this end, various Bayesian optimization
algorithms are tested on simulations that are designed to resemble the aforementioned online
optimization objective. The compared optimization algorithms differ in the surrogate models,
replication schemes and selection strategies that have been used. As it is impossible to change
the stimulus characteristics that have been used to record existing datasets, the simulated objective
functions model the response discriminability as a function of the decoding parameters.
To assess the effectiveness of the optimization algorithms, the algorithms are tested on one, two
and seven-dimensional objective functions. The results suggest that a Bayesian optimization
algorithm that is enriched with heteroskedastic Gaussian process regression and the locationbased
evaluation of existing designs significantly outperforms the random sampling baseline for
all objective functions that have been considered. Even though the thesis is focused at braincomputer
interfaces, application of the results is widespread since heteroskedastic noise can also
be encountered with black-box optimization within other domains.
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Faculteit der Sociale Wetenschappen