Exploring optimal codes and ensemble classifiers in a c-VEP BCI
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2022-06-27
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en
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Abstract
Brain-computer interfaces (BCIs) are used to interact with a computer using brain activity.
BCIs based on code-modulated visual evoked potentials (c-VEPs), whereby the background
of targets flickers with different codes, have recently shown the ability to outperform other
methods in terms of accuracy and speed. In a speller application, electroencephalogram
(EEG) data is recorded from the user while they focus on a target. Though c-VEP-based
BCIs outperform other methods, the accuracy and speed could still be improved. The present
research aims is to improve the accuracy and speed of classification in an offline analysis of two
datasets. First, to explore improving the accuracy, combining multiple canonical correlation
analysis (CCA, a feature extractor) components in an ensemble classifier is shown to not
significantly improve accuracy. Secondly, to improve speed, it is found that there is no
significant difference in the information transfer rate (ITR) of Gold codes within a set. With
a similar goal, amount of neighbours is found to not have a significant effect on the accuracy
of Gold codes. The combination of finding that there is no significant difference in ITR of
Gold codes within a set, and their low cross-correlation properties shows there is no bias in
either. Furthermore, it is found that the amount of neighbours does not significantly affect
Gold codes. Further research could focus on testing these findings on other popular code
families.
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Faculteit der Sociale Wetenschappen