文献类型：期刊文献

英文题名：L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

作者：Zhang, Yu[1];Zhou, Guoxu[2];Jin, Jing[1];Wang, Minjue[1];Wang, Xingyu[1];Cichocki, Andrzej[2,3]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan;[3]Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland

年份：2013

卷号：21

期号：6

起止页码：887

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20134817029053);WOS:【SCI-EXPANDED(收录号:WOS:000327091400003)】；

基金：This work was supported in part by the Nation Nature Science Foundation of China under Grant 61305028, Grant 61074113, Grant 61203127, and Grant 61103122, in part by the Fundamental Research Funds for the Central Universities under Grant WH1314023 and Grant WH1114038, and in part Shanghai Leading Academic Discipline Project B504.

语种：英文

外文关键词：Brain-computer interface (BCI); electroencephalogram (EEG); 11-regularization; multiway canonical correlation analysis (MCCA); steady-state visual evoked potential (SSVEP)

摘要：Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine-cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.