一种无校准的混合方法,将SSVEP和EOG结合起来,用于持续控制
概括
这项研究引入了一种新的脑-计算机接口 (BCI),结合了稳定状态视觉唤起的潜能 (SSVEP) 和电眼镜 (EOG) 来改善连续控制. 新的贝叶斯方法减少了视线转移期间的错误,提高了用户体验和BCI性能.
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:
- 稳态视觉唤起的基于潜力的脑电脑接口 (SSVEP-BCI) 被广泛用于离散控制.
- 连续SSVEP-BCI提供实时命令传递,但在视线转换期间面临过渡状态问题的挑战.
研究的目的:
- 通过混合SSVEP和电眼镜 (EOG) 来开发连续SSVEP-BCI的无校准贝叶斯方法.
- 解决过渡状态问题,提高连续BCI控制的准确性和速度.
主要方法:
- 混合的SSVEP和EOG信号用于连续的BCI控制.
- 使用正规相关性分析 (CCA) 进行SSVEP检测,并采用基于EOG的适应性值方法进行saccade检测.
- 采用贝叶斯优化方法,通过集成SSVEP和saccade数据来识别新的目标.
主要成果:
- 与现有方法 (FBCCA,CCA,MEC,PSDA) 相比,离线实验显示出更高的连续精度和更短的视线转移时间.
- 在线实验表明,与基于CCA的SSVEP-BCI.BCI相比,连续准确度显著更高 (77.61%与68.86%) 和视线转移时间更快 (0.93s与1.94s).
- 参与者报告说,使用拟议的混合BCI,用户体验显著改善.
结论:
- 拟议的混合贝叶斯方法有效地实现了无校准的连续BCI控制.
- 这一框架加强了SSVEP和EOG的集成,促进了连接式BCI用于连续应用.
- 该研究验证了一种用于提高SSVEP-BCI系统稳定性和可用性的新方法.
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