文献类型：期刊文献

英文题名：FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS

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

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama, Japan;[3]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland

年份：2014

卷号：24

期号：4

外文期刊名：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

收录：;EI(收录号：20141617572533);WOS:【SCI-EXPANDED(收录号:WOS:000334281800003)】；

基金：The authors sincerely thank the editor and the anonymous reviewers for their insightful comments and suggestions that helped improve the paper. This study was supported in part by the National Natural Science Foundation of China under Grant 61305028, Grant 61074113, Grant 61203127, Grant 61103122, Grant 61202155, Fundamental Research Funds for the Central Universities Grant WH1314023, Grant WH1114038, and Shanghai Leading Academic Discipline Project B504.

语种：英文

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

摘要：Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.