标题：Sparse Optimal Score based on Generalized Elastic Net Model for Brain Computer Interface
作者：Wu, Qiang; Zhang, Yu; Liu, Ju; Sun, Jiande; Li, Jie
作者机构：[Wu, Qiang; Liu, Ju; Sun, Jiande] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Zhang, Yu] East China Univ Sci & Techn 更多
会议名称：6th International Conference on Information Science and Technology (ICIST)
会议日期：MAY 06-08, 2016
来源：2016 SIXTH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST)
关键词：event-related potential (ERP); brain computer interface (BCI); sparse; constraint; discriminant analysis; optimal scoring; generalized elastic; net
摘要：Brain computer interface (BCI) offers disabled people a nonmuscular communication pathway. Event-related potential (ERP) is an efficient way to achieve the BCI system. One of important issues for ERP classification is the under sample problem, that is the feature dimension is very high while the sample number is very strictly limited. In this paper, we introduce a P300 feature extraction and classification framework using the sparse optimal score method for discriminative analysis by generalized elastic net model. In order to break the curse of dimension, regularized estimation of within-class covariance matrix is achieved and It penalty is applied to learn sparse discriminant vectors. The optimization problem is solved by the alternating least square procedure. We test the proposed framework on P300 target detection task and experimental results indicate that it is able to improve the classification accuracy in P300-based BCI system. The efficient features extracted by our proposed framework provide overall better P300 classification accuracy than several baseline methods especially in the single trial and few training samples case.