Electroencephalogram (EEG), which is widely used for brain computer interface (BCI) systems for input signal, is easily interrupted by physical or mental tasks, and contaminated with various artifacts including power line noise, electromyogram and electrocardiogram. Therefore, such kind of artifacts cause to decrease the accuracy rate and motivate the researchers substantially develop the performance of all components of the communication system between the subject and a BCI output device. So, it is vital to use the most suitable classification algorithm and fewer feature set to implement a fast and accurate BCI system. Addition to this, it is worthwhile mentioning that the classifiers should have the ability for recognizing signals which are collected in different sessions to make brain computer interfaces practical in use. In this study, we proposed fast and accurate classification method for classifying EEG data of up/down/right/left computer cursor movement imagery. EEG signals were collected from three healthy male adults and on two different offline sessions with about one week of delay. The average test classification accuracy calculated as 53.07%.