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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Communication dans un congrès
Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.122-130, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_11〉
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Soumis le : mardi 5 décembre 2017 - 14:58:43
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Izabela Rejer, Robert Burduk. Classifier Selection for Motor Imagery Brain Computer Interface. Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.122-130, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_11〉. 〈hal-01656244〉

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