Motor Imagery BCI Research Based on Hilbert-Huang Transform and Genetic Algorithm

Brain Computer Interface (BCI) based on motor imagery can translate the subject´s EEG, which is captured from the scalp when they are imaging the movements of their limb, into a series of control signals. The patients suffered from locked in syndrome can use this BCI system communicates with the world. Due to the characters of non-linear and non-stationary with the human EEG, how to extract the valuable features from different EEG data based on motor imagery, will be the key problem to design an efficient BCI system. In this paper, a novel method named Hilbert Huang transform (HHT) is used to extract the features from different EEG data based on motor imagery. Genetic algorithm (GA) is used to select the most valuable features to release the pressure of the classifier for higher accuracy and faster speed. Compared with traditional frequency feature extraction method, HHT and GA gain much higher classification accuracy.