Single-trial identification of motor imagery EEG based on HHT and SVM

Abstract

Single-trial identification of motor imagery (MI) EEG is one of the key techniques in the brain-computer interface (BCI). To improve the accuracy of classification and reduce the algorithm time, targeting at motor imagery (MI) EEG of four kinds of motion, a single-trial identification algorithm of MI EEG based on HHT and SVM is proposed. Firstly, MI EEG is decomposed into 8-order intrinsic mode function (IMF) and margin R by empirical mode decomposition (EMD). Secondly, Hilbert spectrum is got by Hilbert transformation. AR model parameter of the extracted 6-order IMF is extracted. The acquired 6-order AR parameter and the characteristic quantity of 29 power spectral density included in the 4-32 Hz EEGs constitute a 35 dimensional characteristic vector. Finally, support vector machine (SVM) is used to classify. The single-trial identification results are as follows: the average recognition rate of the two kinds of thinking actions is 91.6478 %, and that of three is 89.4798 %, four is 89.4064 %. © 2013 Springer-Verlag.