Brain-computer interfaces (BCIs) are a technology that offers the consumer an opportunity manner of communicating with computers. Implementation of a BCI system involves measuring the electrical activity of the brain (EEG) and converting it into output commands. EEG with the mental simulation of hand movements (Motor Imagery EEG) can be used for BCI. In this proposed work the recorded Motor Imagery EEG of several subjects is taken as a database, and the signals are pre-processed by applying filters. Common Spatial Patterns (CSP) and CSPL are extracted. By varying the filter value from 5-15 Hz, for a single subject, 150 samples were recorded from 15 channels with a minimum of 14 features taken as input to the Classification Learner. Optimizable SVM is used for Classification. The classification accuracy is high at 76 when the filter value is 7. © 2023 IEEE.