Research on Performance Evaluation of P300 Brain-computer Interface Under Environment Modeling and Simulation

Authors

• Xiaofei Ge, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Jinling Lian, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Jin Han, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Xin'an Fan, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Danmei Luo, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Yuxiang Hua, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Hao Liu, Beijing Institute of Mechanical Equipment, Beijing 100854, China
• Lijian Zhang, Beijing Institute of Mechanical Equipment, Beijing 100854, China

Abstract

Abstract: In the process of brain-computer interface(BCI) technology stepping from laboratory to practical application scenarios, it is difficult to make an accurate prediction and evaluation of the effect of real environment factors on the system performance. Therefore, a research method based on a multifactor simulation experiment was proposed. A controllable simulation experiment environment was constructed, and the parametric modeling of two key physical environmental factors, namely noise and light, was carried out. By taking the number of electroencephalogram channels as the system parameter, this paper systematically studied the influence mechanism of the aforementioned factors on the decoding performance of P300-BCI. The simulation experiment results show that noise has a significant impact on the decoding performance of the system. The accuracy is higher in a low noise environment, compared with that in a high noise environment. The light factor has no obvious effect on the system performance. As the number of decoding channels decreases, the accuracy shows a corresponding decline. In addition, no significant difference is observed in decoding performance between three channels and all channels. This study not only provides empirical data for the performance modeling of the BCI system in a non-ideal environment but also provides methodological reference for the adaptive design and simulation verification of this technology in real scenarios.

Recommended Citation

Ge, Xiaofei; Lian, Jinling; Han, Jin; Fan, Xin'an; Luo, Danmei; Hua, Yuxiang; Liu, Hao; and Zhang, Lijian (2026) "Research on Performance Evaluation of P300 Brain-computer Interface Under Environment Modeling and Simulation," Journal of System Simulation: Vol. 38: Iss. 3, Article 15.
DOI: 10.16182/j.issn1004731x.joss.25-0098
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss3/15

First Page

746

Last Page

757

CLC

TP391.9

Recommended Citation

Ge Xiaofei, Lian Jinling, Han Jin, et al. Research on Performance Evaluation of P300 Brain-computer Interface Under Environment Modeling and Simulation[J]. Journal of System Simulation, 2026, 38(3): 746-757.

Corresponding Author

Lian Jinling

DOI

10.16182/j.issn1004731x.joss.25-0098