title: SSVEP recognition using common feature analysis in brain-computer interface.
abstract: 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.
  
  
  METHODS
  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).
  
  
  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 1s).
  
  
  CONCLUSIONS
  The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI.
authors.affiliation: Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China. Electronic address: yuzhang@ecust.edu.cn.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan. Electronic address: zhouguoxu@brain.riken.jp.
authors.forename: Guoxu
authors.initials: G
authors.lastname: Zhou
authors.affiliation: Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China. Electronic address: jinjingat@gmail.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Jin
authors.affiliation: Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China. Electronic address: xywang@ecust.edu.cn.
authors.forename: Xingyu
authors.initials: X
authors.lastname: Wang
authors.affiliation: Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan; System Research Institute, Polish Academy of Sciences, Warsaw 00-901, Poland. Electronic address: a.cichocki@riken.jp.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated; D010775:Photic Stimulation; D055815:Young Adult
keywords: Brain–computer interface (BCI); Canonical correlation analysis (CCA); Common feature analysis (CFA); Electroencephalogram (EEG); Steady-state visual evoked potential (SSVEP)