The potential of brain-computer interfaces (BCI) in serving a useful purpose, e.g., supporting communication in paralyzed patients, hinges on the quality of the classification of the brain waves. This paper proposes a novel method to construct a classifier with improved generalization performance. A feature selection method is applied to features calculated from the EEG signals so that unnecessary or redundant features can be removed and only effective features are left for the classification task. Kernel support vector machines (kernel SVM) were used as a classifier and the best combinations of features were searched by backward stepwise selection, i.e., by eliminating unnecessary features one by one, and by evaluating the resulting generalization performance through cross validation. Experiments showed that the generalization performance of the classifier constructed from the best set of features was higher than that of the classifier using all features.
['Kenji Tanaka', 'Takio Kurita', 'Friedrich Meyer', 'Luc Berthouze', 'Tohru Kawabe']
Stepwise Feature Selection by Cross Validation for EEG-based Brain Computer Interface