Farina Dario, Rehbaum Hubertus, Holobar Aleš, Vujaklija Ivan, Jiang Ning, Hofer Christian, Salminger Stefan, van Vliet Hans-Willem, Aszmann Oskar C
IEEE Trans Neural Syst Rehabil Eng. 2014 Jul;22(4):810-9. doi: 10.1109/TNSRE.2014.2306000. Epub 2014 Feb 12.
Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition > 90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons.
靶向肌肉再支配（TMR）将因截肢而失去目标的神经重新导向残端区域内剩余的肌肉，目的是建立直观的肌电信号以控制复杂的假肢装置。为了直接恢复肢体运动背后的神经编码，在本文中，我们展示了从三名TMR患者检测到的高密度表面肌电图（EMG）信号分解为单个运动单位放电序列。目的是首次证明解码TMR患者缺失肢体肌肉神经驱动的可行性，展示解码的准确性，并证明提取的运动单位池的代表性。在每位患者的再支配肌肉上安装6至7个灵活的肌电电极网格，每个网格有64个电极，从而产生多达448个肌电信号。要求受试者尝试对其缺失肢体进行肘部伸展和屈曲、手部张开和闭合、腕部伸展和屈曲、腕部旋前和旋后动作。使用卷积核补偿技术对肌电信号进行分解，并使用基于信号的准确性指标——脉冲噪声比（PNR）评估分解精度。结果表明，每个任务可识别出3至27个运动单位的放电序列，PNR显示分解灵敏度>90%。运动单位放电率在正常支配肌肉的生理值范围内。此外检测到的运动单位显示出高度的共同驱动，因此每个任务提取的单位集代表了活跃单位群体的行为。这些结果为新一代人机界面开辟了一条道路，在这种界面中，控制信号从无创记录中提取，获得的神经信息直接基于运动神经元的放电序列。
