  Brain-Computer Interface (BCI) uses brain signals in order to provide a new
method for communication between human and outside world. Feature extraction,
selection and classification are among the main matters of concerns in signal
processing stage of BCI. In this article, we present our findings about the
most effective features and classifiers in some brain tasks. Six different
groups of classical features and twelve classifiers have been examined in nine
datasets of brain signal. The results indicate that energy of brain signals in
{\alpha} and \b{eta} frequency bands, together with some statistical parameters
are more effective, comparing to the other types of extracted features. In
addition, Bayesian classifier with Gaussian distribution assumption and also
Support Vector Machine (SVM) show to classify different BCI datasets more
accurately than the other classifiers. We believe that the results can give an
insight about a strategy for blind classification of brain signals in
brain-computer interface.
