Brain Pattern Recognition: An evaluation of how the choice of training data affect classification accuracy forinexperienced BCI-users
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
The method used was to create an ensemble of classifiers, one for each time sample of asingle trial and thereafter using majority vote to decide the class of the trial. Classifiersused were support vector machines (SVMs) and linear discriminant analysis (LDA). Foreach subject data for 3 sessions were used, labeled A, B and C in chronological order.Session A and B were used as training sets, and session C as the test data. The result inthis study could not confirm the results of Herman et. al (2008) instead a slight positiveeffect of session A (average CA on session C 62%) compared to session B (average CAon session C 58%) could be seen, but in general there was no big difference in CA basedon the choice of training data (average CA on session C using training sets:A=62%, B=58%, A&B=61%).Our results show that it is not always the case that training data recorded closer in time to the test data generate higher CA. Therefore we suggest that it could be a safer choice to use more than the latest session as training data. Still more studies are needed to confirmthat using more sessions for training really is better also on data where there is a bigger gap in performance between the latest and earlier sessions.
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IdentifiersURN: urn:nbn:se:kth:diva-157698OAI: oai:DiVA.org:kth-157698DiVA: diva2:771147