Identification of EEG induced by motor imagery based on Hilbert-Huang transform

Brain-computer interface is a revolutionary human-computer interaction. The brain-computer interface based on electroencephalogram (EEG) induced by motor imagery (MI) is a very important kind of brain-computer interface. The purpose of this paper is to explore the effective features extraction method for EEG induced by motor imagery. Hilbert-Huang transform (HHT) is used, which has a high resolution both in time domain and frequency domain. Auto regressive (AR) parameters are then extracted and the average instantaneous energy of motor imagery is calculated. Thus structural feature vector is constructed. Finally, support vector machine (SVM) is used for classification of EEG induced by motor imagery. The results show that for the 5.5 to 7.5 seconds of the trial, the average classification accuracy of HHT feature extraction method is 81.08%, and thus this method has a good adaptability. Moreover, the highest classification accuracy of 87.86% is achieved by HHT which is superior to the feature extraction methods using the traditional wavelet transform and without HTT. For the 8 to 9 seconds of the trial, HHT feature extraction method is also significantly better than other two feature extraction methods. This study confirms that HHT has good feature extraction ability for EEG induced by motor imagery which is nonstationary and nonlinear signal. It also confirms the event-related desynchronization (ERD) phenomenon of motor imagery. It is shown that the performance of brain-computer interaction system based on EEG induced by motor imagery is closely related to the performance of the subject's imagination mental activity. This paper can lay a solid foundation for research of online real-time brain-computer interaction control system based on motor imagery. Copyright © 2015 Acta Automatica Sinica. All rights reserved.