title: A new hybrid BCI paradigm based on P300 and SSVEP.
abstract: BACKGROUND
  P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain-computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored.
  
  
  METHODS
  The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength.
  
  
  RESULTS
  The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm.
  
  
  CONCLUSIONS
  A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification.
  
  
  CONCLUSIONS
  The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.
authors.affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China.
authors.forename: Minjue
authors.initials: M
authors.lastname: Wang
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Ian
authors.initials: I
authors.lastname: Daly
authors.affiliation: Cognitive Neuroscience Laboratory, Department of Cognitive Science, University of California at San Diego, La Jolla, CA, USA.
authors.forename: Brendan Z
authors.initials: BZ
authors.lastname: Allison
authors.affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China. Electronic address: jinjingat@gmail.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Jin
authors.affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China.
authors.forename: Lanlan
authors.initials: L
authors.lastname: Chen
authors.affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China. Electronic address: xywang@ecust.edu.cn.
authors.forename: Xingyu
authors.initials: X
authors.lastname: Wang
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D010775:Photic Stimulation; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: Brain–computer interface (BCI); Hybrid paradigm; P300; SSVEP