EEG Based Motor Imagery Classification Using SVM and MLP

This paper focuses on the classification of motor imagery of the left-right hand movements from a healthy subject. Elliptic Bandpass filters are used to discard the unwanted signals. Our study was on C3 and C4 electrodes particularly for the left-right limb movements. We deployed various feature extraction techniques on the EEG data. Statistical-based, wavelet-based energy-entropy & RMS, PSD based average power and bad power were performed to form the desired feature vectors. Variants of Support Vector Machines (SVM) were employed for classification and the results were also compared with Multi-layered Perceptron (MLP). Empirical results show that both SVM and MLP were suitable for such motor imagery classifications with the accuracy of 85% and 85.71% respectively. Among all employed feature extraction techniques wavelet-based methods specifically the energy-entropy feature set, gave promising results for both the classifiers.