Exploring code families and event-types for cVEP BCIs
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2021-06-25
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en
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Abstract
A brain-computer interface (BCI) is a system that makes it possible for a
user to interact with a computer without muscular control. Code-modulated
visual evoked potential (cVEP) BCI can be used to create visual speller systems.
To make a step towards practical, real-world cVEP BCI by improving
performance and reducing calibration time, this thesis compares three methods
of decomposing codes, called event-types, when using the reconvolution
method by Thielen et al. (2015) [12] to generate templates. This is done on
two di erent data sets, one using random stimulation codes and one using
modulated Gold codes. It is found that event-types that take into account the nonlinearity of
the visual system perform better in terms of classi cation accuracy, auto
explained variance and cross explained variance than event-types that assume
linearity. It is concluded that using the edge responses of a
ash as an event-type is the most practical approach, as it models only two events
and therefore requires less training data than event-types with more events.
The contrast event-type is also more versatile, as it does not require events
in the validation set to have appeared in the training set.
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Faculteit der Sociale Wetenschappen