基于SVM概率输出模型的脑控制车辆的侧向控制
Hongguang Pan1,2, Hongzheng Gao1, Zesheng Liu3
1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, China.
概括
这项研究通过使用新型SSVEP接口和概率SVM来提高脑控制车辆 (BCV) 的性能. 新方法提高了车道保持准确性和控制流性,大大提高了BCV的能力.
科学领域:
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
背景情况:
- 脑控制车辆 (BCV) 提供了新的移动解决方案.
- 现有的BCV系统在控制准确性和流性方面面临挑战.
- 稳态视觉唤起潜力 (SSVEP) 接口对BCV控制有希望.
研究的目的:
- 通过使用SSVEP接口来增强BCV的横向控制.
- 为了改善大脑信号解码,并使车辆控制更顺.
- 在BCV环境中评估概率支持向量机 (SVM) 的性能.
主要方法:
- 利用过器银行常见空间模式 (FBCSP) 算法来改进大脑信号解码.
- 开发了一种配备Sigmoid的SVM (SF-SVM),用于生成概率控制命令.
- 进行在线测试以评估分类准确性和车道保持性能.
主要成果:
- 在在线测试中获得了84.03%的分类准确率.
- 与标准的SVM相比,SF-SVM提高了20%以上的车道维护任务完成率.
- 证明了EEG非静止性的减少效应和优化的连续控制.
结论:
- 拟议的SSVEP与SF-SVM的接口显著增强了BCV的侧向控制.
- 概率控制命令导致车辆运行更顺,更准确.
- 这种方法为现实世界的应用中先进的大脑-计算机接口提供了一个有希望的方向.
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