Comparative Study on EEG Based Motor Movement Classification Using Different Sets of Electrode Channels

Abstract

In Brain Computer Interface (BCI), for precise prediction of brain activity, it is important to know which part of the brain is responsible for which activity. Electroencephalography (EEG) signal which conveys the information of such brain activity is recorded using a number of electrodes from all over the skull. In this study, a comparison from a machine learning perspective has been made to investigate which sets of electrodes that mean which part of the brain shows more neural activity during execution or imagination of fist movement. Here, all the preprocessing steps have been done using EEGLAB on MATLAB, and the normalized band powers of five brain rhythms such as alpha, beta, gamma, delta and theta have been used as features. Finally, a supervised machine learning technique – Support Vector Machine (SVM) has been implemented which took those features as input for classification. This study shows that the channel set with more electrodes can distinguish between executed and imaginary fist movement more accurately. Therefore, these findings can be used to understand brain functionality more distinctly and be applied to predict motor movement more precisely in future BCI research.