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An EMD and IMF Energy Entropy Based Optimized Feature Extraction and Classification Scheme for Single Trial EEG Signal


 For the purpose of improving the classification accuracy of single trial EEG signal during motor imagery (MI) process, this study proposed a classification method which combined IMF energy entropy and improved EMD scheme. Singular value decomposition (SVD), Gaussian mixture model, EMD and IMF energy entropy were employed for the newly designed scheme. After removing noise and artifacts from acquired EEG signals in EEGLAB, SVD was applied, and the singular values were clustered by Gaussian mixture model. The insignificant characteristics indicated by the small SVD values were then removed, and the signals were reconstructed, feeding to EMD algorithm. Those IMFs mapping to δ、θ、α and β frequencies were selected as the major features of the EEG signal. The SVM classifier with RBF, linear, and polynomial kernel functions and voting mechanism then kicked in for classification. The results were compared with that of the traditional EMD and EEMD through simulation, showing that the proposed scheme can eliminate mode mixing effectively and improve the single trial EEG signal classification accuracy significantly, suggesting the probability of designing a more efficient EEG control system based on the proposed scheme.