Motor Imagery Classification for BCI Using Common Spatial Patterns and Feature Relevance Analysis

Abstract

Recently, there have been many efforts to develop Brain Computer Interface (BCI) systems, allowing to identify and discriminate brain activity. In this work, a Motor Imagery (MI) discrimination framework is proposed, which employs Common Spatial Patterns (CSP) as preprocessing stage, and a feature relevance analysis approach based on an eigendecomposition method to identify the main features that allow to discriminate the studied EEG signals. The CSP is employed to reveal the dynamics of interest from EEG signals, and then we select a set of features representing the best as possible the studied process. EEG signals modeling is done by feature estimation of three frequency-based and one time-based. Besides, a relevance analysis over the EEG channels is performed, which gives to the user an idea about the channels that mainly contribute for the MI discrimination. Our approach is tested over a well known MI dataset. Attained results (95.21±4.21 [%] mean accuracy) show that presented framework can be used as a tool to support the discrimination of MI brain activity.