Classifying Shapes Made by Continuous Hand Movements Using EEG and the LEAP Motion

dc.contributor.advisorFarquhar, J.D.R.
dc.contributor.advisorVuurpijl, L.G.
dc.contributor.authorIersel, S.C.J.L. van
dc.description.abstractBradberry et al. have shown that electroencephalography (EEG) can be used for reconstructing hand-movements. In this paper, we researched if it is possible to classify shapes that are drawn at different paces. We have made use of the Leap Motion controller, a device that can track hand- and finger-movements. We explored the controller to see if it can be used in 3D movement experiments. We found that there is interference with solid objects and checked the inconsistency of the sampling frequency as stated by Guna et al. [1]. For the classification of the shapes we used two cases: a general case with all paces combined and a separated case where all paces were analyzed separately. We found that for the EEG in the general case only one shape had performances significantly better than chance and in the separated case only one pace was significantly better than chance. We also found that the Leap Motion controller has some flaws that make it difficult for using in 3D movement experiments.en_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.titleClassifying Shapes Made by Continuous Hand Movements Using EEG and the LEAP Motionen_US
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