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基于c-VEP的大脑计算机接口的非参数早期停止检测:试点研究

Victor Martinez-Cagigal, Eduardo Santamaria-Vazquez, Sergio Perez-Velasco

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    这项研究引入了一种新的早期停止算法,用于使用代码调制的视觉唤起潜力 (c-VEP) 的脑机接口 (BCI) 系统. 该方法可以实现更快,更可靠的命令选择,使BCI技术更容易获得.

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    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:

    • 代码调制的视觉唤起潜能 (c-VEP) 为脑计算机接口 (BCI) 提供高精度和短校准时间.
    • 民主化BCI使用需要插件解决方案,需要早期停止算法来进行实时可靠的选择.
    • 在循环转移范式下的c-VEP BCI现有的早期停止技术是有限的.

    研究的目的:

    • 为c-VEP BCI系统提出一种新的非参数早期停止方法.
    • 为了实现可靠的命令选择,实时检测最小代码重复次数.
    • 为了减少c-VEP BCI的选择时间,而不会影响准确性.

    主要方法:

    • 开发了一种新的非参数早期停止算法.
    • 算法将无人看护的命令分布与正常分布相近.
    • 当命令的相关性被确定为异常值时,命令选择会被触发.

    主要成果:

    • 与15名健康用户进行的离线评估,平均准确率为97.08%,速度为1.37秒/命令.
    • 用一个用户进行的在线评估证明了技术可行性,达到96.88%的准确性和1.67秒/命令.
    • 拟议的算法显著减少了选择时间,同时保持了高精度.

    结论:

    • 开发的早期停止算法可用于c-VEP BCI的实时应用.
    • 这种进步可以显著减少命令选择所需的时间.
    • 该方法为更容易访问和用户友好的BCI系统提供了一个实际的步骤.