文献类型：期刊文献

英文题名：SSVEP recognition using common feature analysis in brain-computer interface

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, 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：2015

卷号：244

起止页码：8

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000353754900003)】；

基金：This study was supported in part by the Nation Nature Science Foundation of China under Grant Nos. 61305028, 61074113, 61203127, 61103122, Fundamental Research Funds for the Central Universities under Grant Nos. WH1314023 and WH1114038.

语种：英文

外文关键词：Brain-computer interface (BCI); Electroencephalogram (EEG); Canonical correlation analysis (CCA); Common feature analysis (CFA); Steady-state visual evoked potential (SSVEP)

摘要：Background: Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. New method: We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Results: Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Comparison with existing methods: Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1 s). Conclusions: The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI. (C) 2014 Elsevier B.V. All rights reserved.