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.
CITATION STYLE
Rejer, I., & Burduk, R. (2017). Classifier selection for motor imagery brain computer interface. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10244 LNCS, pp. 122–130). Springer Verlag. https://doi.org/10.1007/978-3-319-59105-6_11
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