An ERP-based BCI with peripheral stimuli: validation with ALS patients

  • Yangyang Miao
  • Erwei Yin
  • Brendan Z. Allison
  • Yu Zhang
  • Yan ChenEmail author
  • Yi Dong
  • Xingyu Wang
  • Dewen Hu
  • Andrzej Chchocki
  • Jing JinEmail author
Research Article


Many studies reported that ERP-based BCIs can provide communication for some people with amyotrophic lateral sclerosis (ALS). ERP-based BCIs often present characters within a matrix that occupies the center of the visual field. However, several studies have identified some concerns with the matrix-based approach. This approach may lead to fatigue and errors resulting from flashing adjacent stimuli, and is impractical for users who might want to use the BCI in tandem with other software or feedback in the center of the monitor. In this paper, we introduce and validate an alternate ERP-based BCI display approach. By presenting stimuli near the periphery of the display, we reduce the adjacency problem and leave the center of the display available for feedback or other applications. Two ERP-based display approaches were tested on 18 ALS patients to: (1) compare performance between a conventional matrix speller paradigm (Matrix-P, mean visual angle 6°) and a new speller paradigm with peripherally distributed stimuli (Peripheral-P, mean visual angle 8.8°); and (2) assess performance while spelling 42 characters online continuously, without a break. In the Peripheral-P condition, 12 subjects attained higher than 80% feedback accuracy during online performance, and 7 of these subjects obtained higher than 90% accuracy. The experimental results showed that the Peripheral-P condition yielded performance comparable to the conventional Matrix-P condition (p > 0.05) in accuracy and information transfer rate. This paper introduces a new display approach that leaves the center of the monitor open for feedback and/or other display elements, such as movies, games, art, or displays from other AAC software or conventional software tools.





This work was supported by the National key research and development program 2017YFB13003002. This work was also supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 61573142, 61773164 and 91420302, and the programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and TechnologyShanghaiPeople’s Republic of China
  2. 2.Unmanned Systems Research CenterNational Institute of Defense Technology Innovation, Academy of Military Sciences ChinaBeijingPeople’s Republic of China
  3. 3.Tianjin Artificial Intelligence Innovation Center (TAIIC)TianjinPeople’s Republic of China
  4. 4.Department of Cognitive ScienceUniversity of California at San DiegoSan DiegoUSA
  5. 5.Department of Psychiatry and Behavior SciencesStanford UniversityStanfordUSA
  6. 6.Department of Neurology, Huashan HospitalFudan UniversityShanghaiPeople’s Republic of China
  7. 7.College of Mechatronic Engineering and AutomationNational University of Defense Technology ChangshaHunanPeople’s Republic of China
  8. 8.Skolkowo Institute of Science and Technology (SKOLTECH)MoscowRussia
  9. 9.Systems Research Institute PASWarsawPoland
  10. 10.Nicolaus Copernicus University (UMK)TorunPoland

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