I. Introduction
EEG is one of the most common non invasive methods for obtaining brain signal. As EEG signals can be used to interpret human intentions, it provides a potential non muscular communication channel for severely disabled people. The new communication channel called Brain Computer Interface (BCI) extracts the neural information hidden in EEG signal to control external devices such as a computer, wheelchair, neuroprothesis etc. Various neurological phenomena, such as visually evoked potentials, slow cortical potentials, P300 potentials, mu and/or beta rhythms and event related (de-) synchronization (ERD/ERS) are used by various researchers to carry out BCI tasks [1]–[4]. In EEG-based BCIs, motor imagery (MI) patterns are commonly used for extracting the neural information. Its operation is based on a rhythmic power decrease or increase in counter-lateral primary sensorimotor areas during preparation for actual movement or imagination of a movement, which are called event related desynchronization (ERD) and event related synchronization (ERS) respectively [4]. The predominant frequency bands of these patterns are very much subject-dependent and it introduces a high inter-subj ect variability in MI-based BCIs.