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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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Submitted on : Tuesday, December 5, 2017 - 2:58:43 PM
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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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