Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition

Abstract

The brain-computer interface is an emerging technology,which can analyze the collected motor imagery signals to control the external auxiliary equipment.A new method based on the noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD) algorithm was proposed for electroencephalogram signal feature extraction and classification.The method outperformed state-of-the-art methods based on noise-assisted multivariate empirical mode decomposition in not only computational efficiency but also classification accuracy.Firstly,all multivariate intrinsic mode functions and trend signals were obtained by the NA-FMEMD.Secondly,the multivariate signals with specific frequency bands were selected by computing their average frequencies.Thirdly,the common spatial pattern was applied to extract features.Finally,the feature vectors were classified using a support vector machine.Simulation data and BCI Competition IV data are used to verify the effectiveness and advantage of the new method,and the method is compared with noise-assisted multivariate empirical mode decomposition.

Keywords

• electroencephalogram signals;motor imagery;noise-assisted fast multivariate empirical mode decomposition;common spatial pattern