title: Enhancing the performance of motor imagery EEG classification using phase features.
abstract: 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.
authors.affiliation: Department of Information Management, National Chung Cheng University, Chiayi County, Taiwan Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi County, Taiwan shenswy@gmail.com shenswy@mis.ccu.edu.tw.
authors.forename: Wei-Yen
authors.initials: WY
authors.lastname: Hsu
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic
keywords: brain–computer interface; electroencephalogram; extreme learning machine; motor imagery