| Abstract: |  This bachelor work deals with comparison of incrementally learnt classifiers using brain activity recordings created by electroencephalograph. It is a complex comparison of classifiers on sleep, artifact and newborn EEG data sets. To reach the best results of classification are also considered the optimal number of attributes. In this work, we used K-Nearest Neighbors classifier and Support Vector Machine classifier. Both of them were implemented into Matlab environment. Matlab was also used for experiments which we had done. The aim of work is to find such classifier, which classification result is the closest to neurologist's evaluation. This should ease analyzing of EEG record for neurologists. The result of this work will be used in PSGlab toolbox for Matlab environment. In this bachelor work we also deal with loading Attribute-Relation File Format (ARFF) files to Matlab environment.
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