title: Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces.
abstract: BACKGROUND
  Steady-state visual evoked potential (SSVEP) has been widely applied to develop brain computer interface (BCI) systems. The essence of SSVEP recognition is to recognize the frequency component of target stimulus focused by a subject significantly present in EEG spectrum.
  
  
  METHODS
  In this paper, a novel statistical approach based on sequence detection (SD) is proposed for improving the performance of SSVEP recognition. This method uses canonical correlation analysis (CCA) coefficients to observe SSVEP signal sequence. And then, a threshold strategy is utilized for SSVEP recognition.
  
  
  RESULTS
  The result showed the classification performance with the longer duration of time window achieved the higher accuracy for most subjects. And the average time costing per trial was lower than the predefined recognition time. It was implicated that our approach could improve the speed of BCI system in contrast to other methods. Comparison with existing method(s): In comparison with other resultful algorithms, experimental accuracy of SD approach was better than those using a widely used CCA-based method and two newly proposed algorithms, least absolute shrinkage and selection operator (LASSO) recognition model as well as multivariate synchronization index (MSI) method. Furthermore, the information transfer rate (ITR) obtained by SD approach was higher than those using other three methods for most participants.
  
  
  CONCLUSIONS
  These conclusions demonstrated that our proposed method was promising for a high-speed online BCI.
authors.affiliation: Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China; Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, D-72074 Tuebingen, Germany. Electronic address: caolei1987@gmail.com.
authors.forename: Lei
authors.initials: L
authors.lastname: Cao
authors.affiliation: Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: albert1991@163.com.
authors.forename: Zhengyu
authors.initials: Z
authors.lastname: Ju
authors.affiliation: Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: nijance@gmail.com.
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: 490597863@qq.com.
authors.forename: Rongjun
authors.initials: R
authors.lastname: Jian
authors.affiliation: Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: cjjiang@tongji.edu.cn.
authors.forename: Changjun
authors.initials: C
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated; D010775:Photic Stimulation; D021641:Recognition, Psychology; D017603:Signal Detection, Psychological
keywords: Brain computer interface (BCI); Canonical correlation analysis (CCA); Sequence detection; Steady-state visual evoked potential (SSVEP)