A hybrid BCI design using cVEP and eye gaze tracking

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dc.contributor.advisorThielen, J.
dc.contributor.advisorTangermann, Michael
dc.contributor.authorPamboer, Luca
dc.date.issued2022-06-27
dc.description.abstractThe code modulated visual evoked potential (cVEP) is a particular response found in electroencephalography (EEG) recordings that can be linked to a specific code represented by a flickering rectangle on an interface. Using this response, a user can control a brain-computer interface (BCI) by shifting gaze location. Eye-tracking (ET) is a method in which the gaze position of a person is predicted based on the position of the pupils. In this study, the benefit of combining cVEP with ET is analysed. The hybrid BCI design makes use of ET to limit the number of commands in both stimulation and classification. In an offline experiment, data from an existing experiment was used, in which 12 participants tested a 6×6 matrix speller BCI. In an analysis on this data, an accuracy of 93% was achieved using a hybrid BCI design with a simulated eye-tracker. Furthermore, an average trial length of 4.29 seconds was reached. Consequently, significant accuracy and ITR improvements were found over a BCI solely based on cVEP. In an online experiment, data was recorded from 8 participants who tested the same 6 × 6 matrix speller BCI. In a post-hoc analysis of the recorded data, a significant increase of 7% in accuracy and 9.70 bits/min for ITR was found when only limiting the classification space. An increase in accuracy and ITR when limiting both stimulation and classification was present but found to be insignificant. The results suggest that limiting the number of commands during stimulation is sub-optimal for the performance of a BCI. In contrast, limiting the commands during classification results in a significant performance improvement in terms of accuracy and ITR. Webcam-based ET could be used to make this limitation.
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15982
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 hybrid BCI design using cVEP and eye gaze tracking
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