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

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. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. 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. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. A 12-target brain-controlled unmanned vehicle online simulation with 12 participants further validated the proposed paradigm and algorithm. In the binocular stimulation paradigm, the average Information Transfer Rate (ITR) reached 154.67±19.69 bits/min in online experiments, with offline training yielding an ITR of 170.7±31.2 bits/min. This novel stimulation paradigm not only supports large-scale target sets for BCI systems but also improves visual comfort, offering stability and feasibility for practical brain-controlled applications. .