Dynamic Stopping for cVEP-based Brain-Computer Interfaces (BCI’s)

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dc.contributor.advisorDesain, P.W.M.
dc.contributor.advisorTangermann, M.W.
dc.contributor.authorHermanns, Kasper
dc.date.issued2022-04-27
dc.description.abstractObjective. In Brain-Computer Interfaces (BCI’s), the most important part of the process is to accurately and quickly classify the brain signals of a user. This helps the system to provide a quick and accurate output and improves user experience. An important part of this classification is deciding when to stop gathering data and producing the output. The literature suggests that ’dynamic stopping’ as opposed to ’static stopping’ methods, have the best performance. A lot of dynamic stopping methods have been created in the literature, but it is unclear what the benefits of these existing methods are. The goal of this research is to get an indication of their relative performance and benefits. Approach. The performance of multiple stopping methods was calculated using offline analysis on a large data set. Furthermore, these methods were all optimized by tweaking their variables to get the optimal performances. This performance was calculated in typed Symbols per minute (SPM) because they were applied to a BCI speller framework. Main results. The results clearly show that using dynamic stopping methods has a positive effect on the performance of a BCI as opposed to using a static stopping method. Every subject would get the best BCI performance by using one of the dynamic methods. Those dynamic methods performed comparable to each other, but have high variance depending on the subject. Optimizing the methods did not create large improvements in performance compared to the training necessary to facilitate them. Only one of the dynamic methods tested did not need long training sessions to facilitate high performance. Significance. The results in this research shows how stopping methods used in cVEP-BCI compare to each other and highlight their individual benefits. It also allows stopping methods designed in future research to be easily compared to its competitors. The code created for this research also facilitates this easy implementation and comparison.
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15862
dc.language.isoen
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.thesis.typeBachelor
dc.titleDynamic Stopping for cVEP-based Brain-Computer Interfaces (BCI’s)

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