The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.
Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH, Zürich 8006, Switzerland.
J Neural Eng. 2021 Apr 6;18(5). doi: 10.1088/1741-2552/abef3a.
. Recent results have shown the potentials of neural interfaces to provide sensory feedback to subjects with limb amputation increasing prosthesis usability. However, their advantages for decoding motor control signals over current methods based on electromyography (EMG) are still debated. In this study we compared a standard EMG-based method with approaches that use peripheral intraneural data to infer distinct levels of grasping force and velocity in a trans-radial amputee.. Surface EMG (three channels) and intraneural signals (collected with transverse intrafascicular multichannel electrodes, TIMEs, 56 channels) were simultaneously recorded during the amputee's intended grasping movements. We sorted single unit activity (SUA) from each neural signal and then we identified the most informative units. EMG envelopes were extracted from the recorded EMG signals. A reference support vector machine (SVM) classifier was used to map EMG envelopes into desired force and velocity levels. Two decoding approaches using SUA were then tested and compared to the EMG-based reference classifier: (a) SVM classification of firing rates into desired force and velocity levels; (b) reconstruction of covariates (the grasp cue level or EMG envelopes) from neural data and use of covariates for classification into desired force and velocity levels.Using EMG envelopes as reconstructed covariates from SUA yielded significantly better results than the other approaches tested, with performance similar to that of the EMG-based reference classifier, and stable over three different recording days. Of the two reconstruction algorithms used in this approach, a linear Kalman filter and a nonlinear point process adaptive filter, the nonlinear filter gave better results.This study presented a new effective approach for decoding grasping force and velocity from peripheral intraneural signals in a trans-radial amputee, which relies on using SUA to reconstruct EMG envelopes. Being dependent on EMG recordings only for the training phase, this approach can fully exploit the advantages of implanted neural interfaces and potentially overcome, in the medium to long term, current state-of-the-art methods. (Clinical trial's registration number: NCT02848846).
. 最近的研究结果表明，神经接口有可能为肢体截肢者提供感觉反馈，从而提高假肢的可用性。然而，与目前基于肌电图（EMG）的方法相比，它们在解码运动控制信号方面的优势仍存在争议。在这项研究中，我们比较了一种基于标准 EMG 的方法与使用外周神经内数据来推断一位桡骨截肢者不同抓握力和速度水平的方法。. 在截肢者预期的抓握运动过程中，同时记录表面肌电图（三个通道）和神经内信号（使用横向纤维内多通道电极，TIMES，56 个通道）。我们对每个神经信号中的单个单元活动（SUA）进行分类，然后确定最具信息量的单元。从记录的 EMG 信号中提取 EMG 包络。使用参考支持向量机（SVM）分类器将 EMG 包络映射到期望的力和速度水平。然后测试并比较了两种使用 SUA 的解码方法与基于 EMG 的参考分类器：（a）通过 SVM 将放电率分类为期望的力和速度水平；（b）从神经数据中重建协变量（抓握线索水平或 EMG 包络），并使用协变量对期望的力和速度水平进行分类。使用 SUA 从神经数据中重建的 EMG 包络作为协变量的方法产生了明显优于其他测试方法的结果，其性能与基于 EMG 的参考分类器相似，并且在三个不同的记录日中保持稳定。在所使用的两种重建算法中，线性卡尔曼滤波器和非线性点过程自适应滤波器，非线性滤波器的效果更好。本研究提出了一种从桡骨截肢者外周神经内信号解码抓握力和速度的新有效方法，该方法依赖于使用 SUA 重建 EMG 包络。该方法仅在训练阶段依赖于 EMG 记录，因此可以充分利用植入式神经接口的优势，并有可能在中长期内克服当前的最新方法。（临床试验注册号：NCT02848846）。
