文献类型：会议论文

英文题名：Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs

作者：Zhang, Yu[1,2];Zhou, Guoxu[1];Zhao, Qibin[1];Onishi, Akinari[1,3];Jin, Jing[2];Wang, Xingyu[2];Cichocki, Andrzej[1]

机构：[1]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama, Japan;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[3]Kyushu Inst Technol, Dept Brain Sci Engn, Fukuoka, Japan

会议论文集：18th International Conference on Neural Information Processing (ICONIP)

会议日期：NOV 13-17, 2011

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Brain-computer interface (BCI); Canonical Correlation Analysis (CCA); Electroencephalogram (EEG); Steady-State Visual Evoked Potential (SSVEP); Tensor Decomposition

摘要：Steady-state visual evoked potential (SSVEP)-based brain computer-interface (BCI) is one of the most popular BCI systems. An efficient SSVEP-based BCI system in shorter time with higher accuracy in recognizing SSVEP has been pursued by many studies. This paper introduces a novel multiway canonical correlation analysis (Multiway CCA) approach to recognize SSVEP. This approach is based on tensor CCA and focuses on multiway data arrays. Multiple CCAs are used to find appropriate reference signals for SSVEP recognition from different data arrays. SSVEP is then recognized by implementing multiple linear regression (MLR) between EEG and optimized reference signals. The proposed Multiway CCA is verified by comparing to the standard CCA and power spectral density analysis (PSDA). Results showed that the Multiway CCA achieved higher recognition accuracy within shorter time than that of the CCA and PSDA.