Enhancing the Performance of Motor Imagery EEG Classification Using Phase Features

An electroencephalogram recognition system considering phase features is proposed to enhance the performance of motor imagery classification in this study. It mainly consists of feature extraction, feature selection and classification. Surface Laplacian filter is used for background removal. Several potential features, including phase features, are then extracted to enhance the classification accuracy. Next, genetic algorithm is used to select sub-features from feature combination. Finally, selected features are classified by extreme learning machine. Compared with without phase features and linear discriminant analysis on motor imagery data from 2 data sets, the results denote that the proposed system achieves enhanced performance, which is suitable for the brain-computer interface applications.