%0 Conference Proceedings
%T Classifier Selection for Motor Imagery Brain Computer Interface
%+ West Pomeranian University of Technology Szczecin
%+ Wroclaw University of Science and Technology
%A Rejer, Izabela
%A Burduk, Robert
%Z Part 2: Biometrics and Pattern Recognition Applications
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Bialystok, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%Y Rituparna Chaki
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-10244
%P 122-130
%8 2017-06-16
%D 2017
%R 10.1007/978-3-319-59105-6_11
%K Imagery brain computer interface
%K Classification
%K Boosting
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01656244/document
%2 https://inria.hal.science/hal-01656244/file/448933_1_En_11_Chapter.pdf
%L hal-01656244
%U https://inria.hal.science/hal-01656244
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-10244