Brain-computer interface paradigms and neural coding

Pengrui Tai; Peng Ding; Fan Wang; Anmin Gong; Tianwen Li; Lei Zhao; Lei Su; Yunfa Fu

doi:10.3389/fnins.2023.1345961

Brain-computer interface paradigms and neural coding

Front Neurosci. 2024 Jan 15:17:1345961. doi: 10.3389/fnins.2023.1345961. eCollection 2023.

Authors

Pengrui Tai^{1

2}, Peng Ding^{1

2}, Fan Wang^{1

2}, Anmin Gong³, Tianwen Li^{2

4}, Lei Zhao^{2

4}, Lei Su^{1

2}, Yunfa Fu^{1

2}

Affiliations

¹ Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
² Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
³ School of Information Engineering, Chinese People's Armed Police Force Engineering University, Xi'an, China.
⁴ Faculty of Science, Kunming University of Science and Technology, Kunming, China.

Abstract

Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.

Keywords: BCI; brain imaging technology; brain-computer interface (BCI) paradigm; neural coding; neural decoding.

Publication types

Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was partially funded by the National Natural Science Foundation of China (Grant Nos. 82172058, 62376112, 81771926, 61763022, and 62366026).