Template Matching for Artifact Detection and Removal

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dc.contributor.advisorDesain, P.W.M.
dc.contributor.advisorVlek, R.J.
dc.contributor.authorBarth, R.
dc.date.issued2009-07-14
dc.description.abstractIn this thesis a method for artifact detection and removal in EEG is presented and tested. This method is based on a template matching technique using cross-correlations in the time domain. A template is created by averaging hand-picked examples of the artifact. Removal of the artifacts is done with use of three di erent template subtraction methods. The quality of the removal is assessed by an averaging paradigm, as well as a frequency analysis. Briefly the algorithm's generalizability is tested with use of a secondary data set containing different artifacts. At last a hypothesis on the source of the artifacts is presented. Results show that template generation, artifact detection as well as removal is successful. Generalizability to other artifacts is good, but performs slightly worse.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/54
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.titleTemplate Matching for Artifact Detection and Removalen_US
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