Neural Engineering Center, Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.
J Neural Eng. 2017 Oct;14(5):056009. doi: 10.1088/1741-2552/aa7d94. Epub 2017 Jul 4.
Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time.
To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms.
The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity.
HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.
多通道袖带电极最近已被研究用于从混合神经记录中提取纤维级别的运动指令。这些信号可以为截肢患者提供对机器人假肢的自主、直观控制。最近的工作已经证明，在急性和慢性制剂中使用空间滤波技术成功地提取了这些信号。然而，这些提取的信号具有较低的信噪比，因此限制了它们在二进制分类中的应用。在这项工作中，提出了一种新的算法，该算法结合了先前的源定位方法，创建了一种基于模型的实时运行的方法。
为了验证该算法，创建了一个盐水台式设置，允许在袖带内精确放置人工源和袖带外的干扰源。人工源取自五秒钟的慢性神经活动，以复制真实的记录。然后，将所提出的算法，混合贝叶斯信号提取（HBSE），与先前的算法、波束形成和贝叶斯空间滤波方法在该测试数据上进行比较。还对所有三种算法分析了一个慢性神经记录示例。
所提出的算法将提取测试信号的信噪比和信号干扰比提高了两到三倍，同时将原始和恢复信号之间的相关系数提高了 10-20%。这些改进在慢性记录示例中得以体现，并增加了恢复信号与记录运动活动之间的计算比特率。
HBSE 在提取现实神经信号方面明显优于先前的算法，即使存在外部噪声源也是如此。这些结果表明，从多束完整神经干中提取动态运动信号是可行的，这反过来又可以为假肢控制的最终目标从截肢者身上提取运动命令信号。
