标题：Regularized Group Sparse Discriminant Analysis for P300-Based Brain-Computer Interface
作者：Wu, Qiang; Zhang, Yu; Liu, Ju; Sun, Jiande; Cichocki, Andrzej; Gao, Feng
作者机构：[Wu, Qiang; Liu, Ju] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Wu, Qiang; Liu, Ju] Shandong Univ, Inst Brain & Bra 更多
通讯作者：Wu, Q;Wu, Q
通讯作者地址：[Wu, Q]Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China;[Wu, Q]Shandong Univ, Inst Brain & Brain Inspired Sci, Jinan, Shandong 更多
来源：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
关键词：Grouped sparse learning; Moreau-Yosida regularization; optimal scoring;; event-related potential (ERP); brain computer interface (BCI)
摘要：Event-related potentials (ERPs) especially P300 are popular effective features for brain-computer interface (BCI) systems based on electroencephalography (EEG). Traditional ERP-based BCI systems may perform poorly for small training samples, i.e. the undersampling problem. In this study, the ERP classification problem was investigated, in particular, the ERP classification in the high-dimensional setting with the number of features larger than the number of samples was studied. A flexible group sparse discriminative analysis algorithm based on Moreau-Yosida regularization was proposed for alleviating the undersampling problem. An optimization problem with the group sparse criterion was presented, and the optimal solution was proposed by using the regularized optimal scoring method. During the alternating iteration procedure, the feature selection and classification were performed simultaneously. Two P300-based BCI datasets were used to evaluate our proposed new method and compare it with existing standard methods. The experimental results indicated that the features extracted via our proposed method are efficient and provide an overall better P300 classification accuracy compared with several state-of-the-art methods.