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Conference Papers Year : 2017

Classifier Selection for Motor Imagery Brain Computer Interface

Abstract

The classification process in the domain of brain computer interfaces (BCI) is usually carried out with simple linear classifiers, like LDA or SVM. Non-linear classifiers rarely provide a sufficient increase in the classification accuracy to use them in BCI. However, there is one more type of classifiers that could be taken into consideration when looking for a way to increase the accuracy - boosting classifiers. These classification algorithms are not common in BCI practice, but they proved to be very efficient in other applications.
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hal-01656244 , version 1 (05-12-2017)

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Izabela Rejer, Robert Burduk. Classifier Selection for Motor Imagery Brain Computer Interface. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. pp.122-130, ⟨10.1007/978-3-319-59105-6_11⟩. ⟨hal-01656244⟩
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