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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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解码由连续的手指运动诱导的脑电图模式,用于脑电脑接口.

Chang Liu1, Jia You2, Kun Wang1

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.

Frontiers in neuroscience
|September 14, 2023
PubMed
概括

这项研究引入了一种新的脑-计算机接口方法,使用连续的手指动作来扩大控制选项. 这种方法对改善神经康复中大脑与计算机接口充满希望.

关键词:
大脑-计算机接口接口电脑电图 (EEG) 是一种电脑电图.与事件相关的脱同步.与运动相关的皮质潜力.按顺序进行手指运动.

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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
  • 康复技术 康复技术 康复技术

背景情况:

  • 基于运动图像的脑计算机接口 (MI-BCI) 显示了神经康复的潜力.
  • 目前的MI-BCI具有有限的可控制指令集,限制了现实世界的应用.
  • 需要新的编码范式来扩展BCI的功能.

研究的目的:

  • 提出和验证基于连续的手指运动的新型运动意图编码范式.
  • 扩展基于运动图像的大脑与计算机接口的指令集功能.
  • 调查使用顺序指动进行BCI控制的可行性.

主要方法:

  • 一个离线实验涉及10名受试者在听觉提示下执行连续的手指按键 (LL,RR,LR,RL).
  • 从EEG数据中分析了与运动相关的皮质潜力 (MRCP) 和与事件相关的脱同步 (ERD) 特征.
  • 一个在线实验与12名受试者验证了范式的实际应用.

主要成果:

  • 无论是MRCP还是ERD特征都表现出不同的时空EEG模式,与不同的顺序指动作任务相对应.
  • 在离线实验中,四个任务的平均分类准确率为71.69%.
  • 在线实验显示,LL-vs-RR的高平均准确率为83.33%,LR-vs-RL的高平均准确率为82.71%.

结论:

  • 拟议的顺序指动模式对于BCI应用是可行的,正如离线和在线实验所证实的那样.
  • 这种方法为优化电动相关EEG信息编码提供了一个有前途的方法.
  • 该研究有助于扩展基于动作意图的BCI的指令集,增强其实用性.