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相关概念视频

Brain Imaging01:14

Brain Imaging

258
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
258

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相关实验视频

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Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
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一个新的非侵入性脑电脑接口,通过想象同度力级别来实现.

Li Hualiang1,2, Ye Xupeng3, Liu Yuzhong1,2

  • 1Key Laboratory of Occupational Health and Safety of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong China.

Cognitive neurodynamics
|July 31, 2023
PubMed
概括

在想象中的握手期间的大脑活动可以预测预期的力水平. 这种基于脑电图 (EEG) 的脑电脑接口 (BCI) 系统在预测力和控制游戏方面取得了很高的准确性.

关键词:
大脑计算机接口 (BCI)电脑电图 (EEG) 是一个电脑电图.同位数的强力力是什么?运动想象力 运动想象力支持矢量机器 (SVM) 是一个支持矢量机器.

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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:

  • 在单边收缩期间,生理电路与同位力力水平有所不同.
  • 准确预测预期的力对于先进的大脑与计算机接口 (BCI) 来说至关重要.
  • 想象运动任务可以产生明显的脑电图 (EEG) 图案.

研究的目的:

  • 预测预期的同位力力级别 (5%对40%MVC) 从单次试验EEG在单边右手握握的想象力.
  • 评估一种新的BCI系统的有效性,用于在线控制球比赛,使用想象中的抓力水平.

主要方法:

  • 使用电脑电图 (EEG) 来自9名健康人群的数据,在5%和40%的最大自愿收缩 (MVC) 时进行单边右手握力想象.
  • 使用共同空间模式 (CSP) 和信号连贯性提取的特征.
  • 采用支持矢量机 (SVM) 分类器来预测力级,并评估在线游戏控制的准确性.

主要成果:

  • 从单次试验EEG来预测力水平,平均准确率为81.4%±13.29%.
  • 该BCI系统实现了有效的在线控制球比赛,达到76.67%的平均准确度±9.35%的方向控制.
  • 数据分析证实了该系统对实时应用的有效性.

结论:

  • 在想象的握力任务中,单次试验EEG可以可靠地预测预期的力水平.
  • 开发的BCI系统展示了直观的在线控制应用程序的潜力,例如游戏.
  • 这项技术为将细微的用户命令集成到机器人控制系统中提供了基础.