SSVEP recognition using common feature analysis in brain–computer interface
SSVEP recognition using common feature analysis in brain–computer interface
复制标题
DOI：
10.1016/j.jneumeth.2014.03.012
复制
发表时间：
2015-04
影响因子：
3
通讯作者：
Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang;A. Cichocki
中科院分区：
文献类型：
--
作者：
Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang;A. Cichocki
背景典型相关分析（CCA）已成功应用于脑-机接口（BCI）应用中的稳态视觉诱发电位（SSVEP）识别。虽然CCA方法通过多通道检测优于传统的功率谱密度分析，但它需要额外的预构造的正余弦波的参考信号。新方法我们认为，同一个被试在某一刺激频率下记录的一组脑电图（EEG）数据试验应该具有一些共同的特征，这些特征可能具有真实的SSVEP特征。因此，本研究提出了一种基于共同特征分析（CFA）的方法，利用潜在的共同特征作为自然参考信号，使用相关分析SSVEP recognization.ResultsGood性能的CFA方法SSVEP识别验证与EEG数据记录从10个健康受试者，与CCA和CCA多路扩展（MCCA）相比，实验结果表明，CFA方法在使用短时间窗口（即，结论CFA方法的优越性表明，该方法有望用于开发基于SSVEP的实时脑机接口。
BackgroundCanonical 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 methodWe 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.ResultsGood 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 methodsExperimental 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).ConclusionsThe superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI.