Motor Imagery is a mental process that includes preparation for movement. The brain interface system intends to prepare direct connectivity between the brain and the computer to be aware of the requests of an individual and use them as a control signal for external devices. Motion imaging events occur in the three main frequency bands: beta, mu, and gamma. After preprocessing the EEG data, the next step is to apply various types of filters in order to reduce any residual noise present in the signal. Numerous functional imaging studies showed that motion-imaging results from the specific activation of neural circuits involved in the early stages of motor control. Studies have shown that the CSP algorithm performs better than other algorithms. Due to the lack of a suitable frequency band, the results of the frequency-dependent CSP method are not satisfactory, so the CSSP is similar to the FIR filter, but since this filter does not have all the coefficients of an FIR filter, the presence of noise in the EEG signal can lead to suboptimal definition of the frequency filter. The CSSSP algorithm was used to solve this problem. With using sequential feature selection for feature extraction, it was revealed that CSSSP performance has been better compared to the CSP and CSSP in most cases and the average accuracy was 92.55%.