Converging Clinical and Engineering Research on Neurorehabilitation III - Proceedings of the 4th International Conference on NeuroRehabilitation (ICNR2018), October 16-20, 2018, Pisa, Italy
Converging Clinical and Engineering Research on Neurorehabilitation III - Proceedings of the 4th International Conference on NeuroRehabilitation (ICNR2018), October 16-20, 2018, Pisa, Italy
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神经康复的融合临床与工程研究 III - 第四届神经康复国际会议 (ICNR2018) 论文集，2018 年 10 月 16-20 日，意大利比萨
DOI：
10.1007/978-3-030-01845-0_27
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Siadatnejad S
中科院分区：
文献类型：
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作者：
Siadatnejad S
本文提出了一种新的数学方法，从高密度表面肌电记录的神经棘波序列中解码信息，用于机械手的控制。在以低发放率为特征的精确抓取过程中，该算法使用点过程方法来提取尖峰时间中编码的信息。当执行能量抓取时，增加的激发速率使得点法过程的可靠性降低，算法自动并逐渐切换到基于肌电功率的方法。与当前依赖于观测模型的高斯近似的点过程译码算法不同，该模型采用了全高斯状态空间表示，这得益于逆高斯分布的速率的参数化以及通过直接对尖峰间隔进行建模。
This paper presents a novel mathematical approach to decode information from neural spike trains recorded using high-density surface EMG for the control of a robotic hand. During precision grasps characterised by low firing rates, the algorithm uses a point-process approach to extract the information encoded in the spike times. When performing power grasps, the increased firing rate makes the point-process less reliable and the algorithm automatically and gradually switches to an approach based on the EMG power. Unlike current point-process decoding algorithm relying on a Gaussian approximations of the observation model, this model adopts a fully Gaussian state-space representation thanks to a parametrization of the rate of an inverse Gaussian distribution and by modelling the inter-spike interval directly.