文献类型：期刊文献

英文题名：A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface

作者：Jiao, Yong[1];Zhang, Yu[1];Wang, Yu[2];Wang, Bei[1];Jin, Jing[1];Wang, Xingyu[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China;[2]Shanghai Ruanzhong Informat Technol Co Ltd, Shanghai, Peoples R China

年份：2018

卷号：28

期号：4

外文期刊名：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

收录：;EI(收录号：20174104268055);WOS:【SCI-EXPANDED(收录号:WOS:000427254300002)】；

基金：This study was supported in part by the National Natural Science Foundation of China under Grants Nos. 61305028, 91420302 and 61573142, the Shanghai Chenguang Program No. 14CG31, the Fundamental Research Funds for the Central Universities under Grants Nos. WH1516018 and 222201717006, the Shanghai Natural Science Foundation under Grant 16ZR1407500, the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

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

摘要：Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.