Template Matching for Artifact Detection and Removal
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2009-07-14
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en
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Abstract
In 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.
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Faculteit der Sociale Wetenschappen