Classification of Three Class Hand Imagery Movement with the Application of 2-Stage SVM Model ☆

Classification of multiple motor states of human brain waves will improve the applicable ability to brain – computer interfacing system and neuroprosthesis. In this research, we aim at extending classifier ability to three class imagery movement states including: left hand imagery movement, right hand imagery movement and rest state. We propose to implement new classifier using combination of discriminated features for input feature vector and classifying model based on 2-stage SVM. For the feature vector construction, ANOVA-based feature selection method is proposed to use which resulting in 14% reduction of number of features required for the process. The avarage classification accuracy of our proposed system is achived with 80,75% when evaluated on Physionet dataset. The study demonstrate that proposed IHMv classifier can distinguish more output classes than other researches with less number of electrodes while maintaining similar accuracy classification. Therefore, our system can be applied to BCI system to create controlling commands to peripheral devices or computer application through motor brain waves.