Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.

Abstract

Objective. With the rapid development of brain-computer interface (BCI) technology, steady-state visual evoked potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort.Approach. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. (UV) This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved filter bank dual-frequency task-discriminant component analysis (FBD-TDCA) algorithm.Main results. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. Furthermore, the FBD-TDCA algorithm outperformed existing methods such as filter bank task-related component analysis and filter bank canonical correlation analysis, achieving a classification accuracy of 89.27% ± 3.67 and an information translate rate of 163.87 ± 14.32 bits min^-1, with statistical significance confirmed through pairedt-tests. The system's practical application was further demonstrated in an online 12-command UV control task. Participants achieved an average classification accuracy of 90.34% ± 8.75%, with most maintaining low path deviation rates during navigation tasks.Significance. The proposed binocular SSVEP stimulation paradigm and FBD-TDCA algorithm address the limitations of traditional methods, offering enhanced command set scalability, improved visual comfort, and superior performance, paving the way for more efficient and user-friendly BCI applications in real-world scenarios.