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Review Article | Open Access

Review of brain–computer interface based on steady-state visual evoked potential

 School of Life Science, Beijing Institute of Technology, Beijing 100081, China
 School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing 100081, China
 Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100981, China
 Kyoto University, Yoshida-honmachi 606-8501, Kyoto-Shi, Japan
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Abstract

The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady-state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal-to-noise ratio and short training-time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

Keywords

steady-state visual evoked potentialbrain–computer interfacecanonical correlation analysisdecoding algorithm

References

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Brain Science Advances
Volume 8 Issue 4,
December 2022
Pages 258-275
DOI: 10.26599/BSA.2022.9050022

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Cite this article:
Liu S, Zhang D, Liu Z, et al. Review of brain–computer interface based on steady-state visual evoked potential. Brain Science Advances, 2022, 8(4): 258-275. https://doi.org/10.26599/BSA.2022.9050022

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Received: 11 July 2022
Revised: 13 September 2022
Accepted: 27 September 2022
Published: 30 November 2022
© The authors 2022.

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).