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对3D神经假肢器件的直接皮质控制.

Dawn M Taylor1, Stephen I Helms Tillery, Andrew B Schwartz

  • 1Department of Bioengineering, Arizona State University, Tempe, AZ 85287-6006, USA.

Science (New York, N.Y.)
|June 8, 2002
PubMed
概括

皮质神经元调特性在实时大脑控制的运动中适应. 适应性算法使得精确的三维神经假肢控制能够使用更少的神经元,在实践中得到改进.

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
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 皮层神经元活动的实时解码使神经假肢设备控制成为可能.
  • 之前的模型假设固定的神经元调特性,受试者不意识到预测的运动.

研究的目的:

  • 为了研究在实时大脑控制的三维 (3D) 运动中皮质神经元调属性的动态变化.
  • 开发和评估可追踪这些神经元变化的自适应控制算法.

主要方法:

  • 实验对象使用实时视觉反来控制3D神经假肢轨迹,对他们的大脑活动进行视觉反.
  • 记录和解码皮质神经元活动使用算法,适应变化的细胞调特性.
  • 运动准确度和神经元调整在每日练习时进行了评估.

主要成果:

  • 皮质神经元调特性被发现在大脑控制的运动中动态变化.
  • 自适应控制算法成功跟踪了这些变化,从而实现了精确的3D运动.
  • 试验对象使用比预期的少得多的皮质单元实现了长时间的3D运动序列.
  • 日常练习提高了运动精度和神经元群体的定向调.

结论:

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  • 神经调节是适应性的,可以用于有效的脑电脑接口.
  • 通过计算动态神经变化,自适应算法对于优化神经假肢控制至关重要.
  • 这种方法为控制复杂的神经假肢设备提供了一种更有效和潜在的更直观的方法.