Multi-feature fusion method based on WOSF and MSE for four-class MI EEG identification

Abstract

BackgroundBrain-Computer Interface (BCI) can bring great convenience to patients in the process of rehabilitation training and prosthetic control. However, how to extract effective features is a core issue in a BCI system. MethodsWe proposes the Multi-Feature Fusion Method based on Wavelength Optimal Spatial Filter and Multiscale Entropy for classifying the electroencephalogram signals (EEG) in four kinds of motor imagery tasks. The method can combine Wavelength features with Multiscale Entropy. ResultsTwo groups of experiments were conducted. One for simple four types of motor imagery (MI) tasks and another for unilateral limb. Experiments show that our proposed method has better performance compared to other methods. (82.55% versus 73.08% average accuracy respectively). ConclusionsThe proposed method can effectively improve the accuracy of EEG classification in multiclass motor imagery and will be useful for neurorehabilitation through motor imagery for hemiplegic patients.