Enhancement Characteristics of Visual Stimulus Elements in SSVEP-BCI System
Recommended citation: Li, Z., Zhou, Z., Wang, Y., Tian, J., Yang, W., Niu, Y. (2023). Enhancement Characteristics of Visual Stimulus Elements in SSVEP-BCI System. In: Tareq Ahram, Waldemar Karwowski, Pepetto Di Bucchianico, Redha Taiar, Luca Casarotto and Pietro Costa (eds) Intelligent Human Systems Integration (IHSI 2023): Integrating People and Intelligent Systems. AHFE (2023) International Conference. AHFE Open Access, vol 69. AHFE International, USA. http://doi.org/10.54941/ahfe1002921 http://george-wyy.github.io/files/paper2.pdf
SSVEP-BCI system has the advantages of few recording electrodes, short training time, strong anti-interference ability, etc. It is widely used in the brain-computer interface (BCI) field. However, this system also has the disadvantage of low recognition efficiency. Its stimulation interface will also cause visual fatigue for the users. Therefore, aiming at the optimization of stimulus elements in the SSVEP-BCI interface, this paper explored the impact of different numbers of auxiliary stimulus particles on the system recognition efficiency and user experience under the same stimulus area. Relevant ergonomic experiments and subjective evaluations were conducted. Ergonomic experiment results show that the number of auxiliary stimulus particles has a significant impact on the recognition efficiency of the system. When the number of auxiliary stimulus particles approaches infinity, the recognition efficiency is the highest. The subjective evaluation results show that the change in the number of auxiliary stimulus particles has a significant impact on the system interface usability index score, and the score is higher when the number is infinite. From the perspective of design ergonomics, this study explored the impact of the number of auxiliary stimulus particles on the efficiency of the brain-computer interface system and user satisfaction, the research conclusions have important guiding significance for optimizing and standardizing the design of the brain-computer interface.