A Deep Learning approach to Noise Tagging

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dc.contributor.advisorThielen, J.
dc.contributor.advisorTangermann, M.W.
dc.contributor.authorGeurts, Siebe
dc.date.issued2021-07-22
dc.description.abstractBrain-computer interfaces (BCI) are systems that allow direct communication between the brain and a computer. A speci c type of BCI is a codemodulated visual evoked potential (cVEP) based spelling BCI. These cVEP BCIs can use di erent approaches to decoding and classifying brain signals. Reconvolution, linear discriminant analysis (LDA) and deep learning are some of these approaches. In my research, I implement my own adaptation of a deep learning model and compare it against a baseline model which is LDA, and I compare both deep learning and LDA against a simple event type reconvolution. The deep learning approach (96.0% accuracy) is shown to be signi cantly better (p < :05) than the simple event type reconvolution (56.8% accuracy). Furthermore, on trial classi cation the deep learning approach (96.0% accuracy) does not perform signi cantly di erent (p > :05) from LDA (95.7% accuracy). On epoch classi cation deep learning (65.3% accuracy) performs signi cantly better (p < :05) than LDA (63.2% accuracy). This proves deep learning to be a useful option for cVEP BCIs with many ways to go further in research.
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15694
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.titleA Deep Learning approach to Noise Tagging
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