EMG and ENG-envelope pattern recognition for prosthetic hand control
EMG and ENG-envelope pattern recognition for prosthetic hand control
复制标题
DOI：
10.1016/j.jneumeth.2018.10.004
复制
发表时间：
2019-01-01
影响因子：
3
通讯作者：
Zollo, Loredana
中科院分区：
文献类型：
--
作者：
Noce, Emiliano;Bellingegni, Alberto Dellacasa;Zollo, Loredana
背景：本文提出了一种手假肢神经控制的新方法，基于应用于神经信号包络（eENG）的模式识别。新方法：通过考虑神经记录中尖峰的幅度和出现来计算 ENG 包络。以应用于肌肉信号的模式识别算法为参考，并与传统采用的神经信号尖峰排序算法（SSA）进行了比较分析。方法验证分为两部分：首先，离线分析一名截肢受试者通过神经内电极记录的神经信号，以区分两个执行的手势；其次，通过合成数据研究了算法性能随着类别数量的增加而衰减。结果：将模式识别应用于eENG，对真实数据的准确率达到98.26%。 SSA 的准确率达到了 70%。增加类的数量会降低该算法的准确性。此外，应用于 eENG 的模式识别的计算时间非常短（分析的数据窗口中每个样本为 32.6 μs）。与现有方法相比：eENG 被证明在解码用户意图方面比 SSA 算法更可靠，并且计算效率更高。结论：结果表明，可以将众所周知的 EMG 模式识别技术应用于方便处理的神经信号，并为神经手势解码在上肢假肢中的应用铺平道路。
Background: This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG).New method: The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data.Results: An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 mu s for each sample in the data window analyzed).Comparison with existing method: The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient.Conclusions: It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics.