Tai Pengrui, Ding Peng, Wang Fan, Gong Anmin, Li Tianwen, Zhao Lei, Su Lei, Fu Yunfa
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
Front Neurosci. 2024 Jan 15;17:1345961. doi: 10.3389/fnins.2023.1345961. eCollection 2023.
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.
脑机接口(BCI)用户中枢神经系统中产生的脑信号模式与BCI范式和神经编码密切相关。在BCI系统中,BCI范式和神经编码是BCI研究的关键要素。然而,到目前为止,很少有参考文献清晰、系统地阐述BCI范式的定义和设计原则以及BCI神经编码的定义和建模原则。因此,本文对这些内容进行了阐述,并在综述中介绍了现有的主要BCI范式和神经编码。最后,讨论了BCI范式和神经编码面临的挑战及未来研究方向,包括以用户为中心的BCI范式和神经编码设计与评估、革新传统BCI范式、突破现有脑信号采集技术以及将BCI技术与先进人工智能技术相结合以提高脑信号解码性能。期望该综述能激发BCI范式和神经编码的创新性研发。