Comparing Classification Methods for Asynchronous Brain-computer Interfaces

dc.contributor.advisorFarquhar, J.D.R.
dc.contributor.advisorRoijendijk, L.M.M.
dc.contributor.authorGrootswagers, T.
dc.date.issued2011-08-29
dc.description.abstractIn the field of Brain-Computer Interfacing, the asynchronous approach adds a new ”no movement” class to the classic trial-based synchronous approach. This ”no movement” class is very unbalanced (most of the data will belong to this class) and therefore causes a major classification obstacle. This study investigates the suitability of different classification methods for use in an asynchronous Brain-Computer Interface. We used a Guitar Hero like game to gather EEG data to compare the classification methods using the Readiness Potential in different movement conditions. Based on the results, we recommend to use a hierarchical method that first classifies between movement and no movement, and only if movement is detected, classifies between left and right movement.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/89
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleComparing Classification Methods for Asynchronous Brain-computer Interfacesen_US
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