研究双眼脑控制无人驾驶车辆的编码和解码算法
Fangzhou Xu1,2,3, Yanbing Liu1, Yanzi Li1
1International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, People's Republic of China.
Journal of neural engineering
|June 25, 2025
概括
这项研究介绍了一种新的双筒定态视觉唤起潜力 (SSVEP) 脑计算机接口 (BCI) 无人驾驶车辆,增强命令集和视觉舒适性. 新系统和算法在模拟和现实任务中实现了高精度.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人与计算机的交互
背景情况:
- 稳态视觉唤起潜力 (SSVEP) 对脑电脑接口 (BCI) 有效.
- 传统的单频SSVEP在命令可扩展性和视觉舒适性方面存在局限性.
- 由大脑控制的无人驾驶汽车 (UVs) 需要高效和舒适的BCI解决方案.
研究的目的:
- 开发一种新的双筒SSVEP刺激范式,用于增强紫外线控制.
- 为了提高基于SSVEP的BCI中的命令集可扩展性和视觉舒适性.
- 引入和验证一个改进的过器银行双频任务区分组件分析 (FBD-TDCA) 算法.
主要方法:
- 一个双眼SSVEP刺激范式,使用棋盘和相位编码,每个目标具有双频率 (30-35Hz).
- 极化光技术为每只眼睛提供不同的频率,减少视觉干扰.
- 一个改进的过器银行双频任务区分组件分析 (FBD-TDCA) 算法用于信号处理.
主要成果:
- 六个频率编码了15个命令,其性能与传统方法相美.
- 该FBD-TDCA算法实现了89.27%±3.67分类准确度和163.87±14.32比特分钟-1的信息传输速率.
- 在线12个命令的紫外线控制任务显示了90.34%±8.75%的准确度,路径偏差很小.
结论:
- 拟议的双筒SSVEP范式增强了指挥可扩展性和视觉舒适性.
- 与现有方法相比,FBD-TDCA算法提供了更高的性能.
- 这种方法推进了高效和用户友好的BCI应用程序,用于现实世界的场景,如UV控制.
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