来自自然传感器的肢体状态反馈解码得到改进。
Improved decoding of limb-state feedback from natural sensors.
作者信息
Wagenaar J B, Ventura V, Weber D J
机构信息
Department of BioEngineering, University of Pittsburgh, Pittsburgh, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4206-9. doi: 10.1109/IEMBS.2009.5333614.
Limb state feedback is of great importance for achieving stable and adaptive control of FES neuroprostheses. A natural way to determine limb state is to measure and decode the activity of primary afferent neurons in the limb. The feasibility of doing so has been demonstrated by [1] and [2]. Despite positive results, some drawbacks in these works are associated with the application of reverse regression techniques for decoding the afferent neuronal signals. Decoding methods that are based on direct regression are now favored over reverse regression for decoding neural responses in higher regions in the central nervous system [3]. In this paper, we apply a direct regression approach to decode the movement of the hind limb of a cat from a population of primary afferent neurons. We show that this approach is more principled, more efficient, and more generalizable than reverse regression.
肢体状态反馈对于实现功能性电刺激（FES）神经假体的稳定和自适应控制至关重要。确定肢体状态的一种自然方法是测量并解码肢体中初级传入神经元的活动。[1]和[2]已经证明了这样做的可行性。尽管取得了积极成果，但这些研究中的一些缺点与应用反向回归技术来解码传入神经信号有关。基于直接回归的解码方法现在比反向回归更受青睐，用于解码中枢神经系统较高区域的神经反应[3]。在本文中，我们应用直接回归方法从一群初级传入神经元中解码猫后肢的运动。我们表明，这种方法比反向回归更具原则性、更高效且更具通用性。
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